Position-grid based machine learning for gnss warm-start position accuracy improvement

ABSTRACT

Aspects presented herein may improve the performance and accuracy of GNSS-based positioning, where a position-grid based ML may be implemented by a UE or a location server to improve the accuracy of identifying a warm-start position of the UE. In one aspect, a UE or a location server determines, for each grid point within a range of an initial position of a UE, a set of PR residuals based on PRs for each SV of a set of SVs. The UE or the location server determines an estimated position of the UE based on the sets of determined PR residuals.

TECHNICAL FIELD

The present disclosure relates generally to positioning systems, andmore particularly, to positioning involving machine learning.

INTRODUCTION

Wireless communication systems are widely deployed to provide varioustelecommunication services such as telephony, video, data, messaging,and broadcasts. Typical wireless communication systems may employmultiple-access technologies capable of supporting communication withmultiple users by sharing available system resources. Examples of suchmultiple-access technologies include code division multiple access(CDMA) systems, time division multiple access (TDMA) systems, frequencydivision multiple access (FDMA) systems, orthogonal frequency divisionmultiple access (OFDMA) systems, single-carrier frequency divisionmultiple access (SC-FDMA) systems, and time division synchronous codedivision multiple access (TD-SCDMA) systems.

These multiple access technologies have been adopted in varioustelecommunication standards to provide a common protocol that enablesdifferent wireless devices to communicate on a municipal, national,regional, and even global level. An example telecommunication standardis 5G New Radio (NR). 5G NR is part of a continuous mobile broadbandevolution promulgated by Third Generation Partnership Project (3GPP) tomeet new requirements associated with latency, reliability, security,scalability (e.g., with Internet of Things (IoT)), and otherrequirements. 5G NR includes services associated with enhanced mobilebroadband (eMBB), massive machine type communications (mMTC), andultra-reliable low latency communications (URLLC). Some aspects of 5G NRmay be based on the 4G Long Term Evolution (LTE) standard. There existsa need for further improvements in 5G NR technology. These improvementsmay also be applicable to other multi-access technologies and thetelecommunication standards that employ these technologies.

BRIEF SUMMARY

The following presents a simplified summary of one or more aspects inorder to provide a basic understanding of such aspects. This summary isnot an extensive overview of all contemplated aspects. This summaryneither identifies key or critical elements of all aspects nordelineates the scope of any or all aspects. Its sole purpose is topresent some concepts of one or more aspects in a simplified form as aprelude to the more detailed description that is presented later.

In an aspect of the disclosure, a method, a computer-readable medium,and an apparatus are provided. The apparatus determines, for each gridpoint within a range of an initial position of a user equipment (UE), aset of pseudorange (PR) residuals based on PRs for each space vehicle(SV) of a set of SVs. The apparatus determines an estimated position ofthe UE based on the sets of determined PR residuals.

To the accomplishment of the foregoing and related ends, the one or moreaspects comprise the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe drawings set forth in detail certain illustrative features of theone or more aspects. These features are indicative, however, of but afew of the various ways in which the principles of various aspects maybe employed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a wireless communicationssystem and an access network.

FIG. 2A is a diagram illustrating an example of a first frame, inaccordance with various aspects of the present disclosure.

FIG. 2B is a diagram illustrating an example of DL channels within asubframe, in accordance with various aspects of the present disclosure.

FIG. 2C is a diagram illustrating an example of a second frame, inaccordance with various aspects of the present disclosure.

FIG. 2D is a diagram illustrating an example of UL channels within asubframe, in accordance with various aspects of the present disclosure.

FIG. 3 is a diagram illustrating an example of a base station and userequipment (UE) in an access network.

FIG. 4 is a diagram illustrating an example of a UE positioning based onreference signal measurements.

FIG. 5 is a diagram illustrating an example of Global NavigationSatellite System (GNSS) positioning in accordance with various aspectsof the present disclosure.

FIG. 6 is a diagram illustrating an example of non-line-of-sight (NLOS)GNSS measurements in an urban area in accordance with various aspects ofthe present disclosure.

FIG. 7 is a diagram illustrating an example architecture of a functionalframework for RAN intelligence enabled by data collection in accordancewith various aspects of the present disclosure.

FIG. 8A is a diagram illustrating an example machine learning (ML)training for an ML classifier that is capable of classifying GNSSpseudorange (PR) measurements in accordance with various aspects of thepresent disclosure.

FIG. 8B is a diagram illustrating an example ML inferencing for an MLclassifier that is capable of classifying GNSS PR measurements inaccordance with various aspects of the present disclosure.

FIG. 9 is a diagram illustrating an example space vehicle (SV) geometryin accordance with various aspects of the present disclosure.

FIG. 10 is a diagram illustrating an example ML training based on agraph convolutional network (GCN) in accordance with various aspects ofthe present disclosure.

FIG. 11 is a diagram illustrating an example architecture ofapproximating GCN classifier based on multilayer perceptron (MLP) inaccordance with various aspects of the present disclosure.

FIG. 12 is a diagram illustrating an example of horizontal errorcumulative distribution functions (CDFs) for ML classifiers based on MLPand GCN in accordance with various aspects of the present disclosure.

FIG. 13 is a diagram illustrating an example of receiver operationcharacteristics for ML classifiers based on MLP and GCN in accordancewith various aspects of the present disclosure.

FIG. 14 is a diagram illustrating an example of known and unknownparameters for a GNSS device during a warm start in accordance withvarious aspects of the present disclosure.

FIG. 15 is a diagram illustrating an example of identifying a positionof a GNSS device based on a position-grid approach in accordance withvarious aspects of the present disclosure.

FIG. 16 is a diagram illustrating an example ML model for inferencingwhether a grid point is approximate to an actual position of a GNSSdevice in accordance with various aspects of the present disclosure.

FIG. 17 is a flowchart of a method of wireless communication.

FIG. 18 is a flowchart of a method of wireless communication.

FIG. 19 is a diagram illustrating an example of a hardwareimplementation for an example apparatus and/or network entity.

DETAILED DESCRIPTION

Aspects presented herein may improve the performance and accuracy ofGNSS-based positioning. Aspects presented herein provide a position-gridbased ML to a GNSS device to improve the accuracy of identifying awarm-start position of the GNSS device. In one aspect, a GNSS device maybe configured to estimate its initial position and establish aposition-grid with a fixed resolution (e.g., with multiple grid points)that covers a region of position uncertainty based on the estimatedinitial position. Then, the GNSS device may compute per SV PRmeasurement residuals at each grid point of the position-grid. An MLmodel may be trained to process per SV GNSS observables and PRmeasurement residuals obtained by the GNSS device to infer/predict theprobability of whether a grid point is near the actual position of theGNSS device. For example, the ML model may be trained based on PR errorestimates derived from atomic clock accurate and/or the known distancebetween the GNSS device position and the grid point position, and the MLmodule may estimate the GNSS device location using the distribution ofgrid point probabilities across the position-grid.

The detailed description set forth below in connection with the drawingsdescribes various configurations and does not represent the onlyconfigurations in which the concepts described herein may be practiced.The detailed description includes specific details for the purpose ofproviding a thorough understanding of various concepts. However, theseconcepts may be practiced without these specific details. In someinstances, well known structures and components are shown in blockdiagram form in order to avoid obscuring such concepts.

Several aspects of telecommunication systems are presented withreference to various apparatus and methods. These apparatus and methodsare described in the following detailed description and illustrated inthe accompanying drawings by various blocks, components, circuits,processes, algorithms, etc. (collectively referred to as “elements”).These elements may be implemented using electronic hardware, computersoftware, or any combination thereof. Whether such elements areimplemented as hardware or software depends upon the particularapplication and design constraints imposed on the overall system.

By way of example, an element, or any portion of an element, or anycombination of elements may be implemented as a “processing system” thatincludes one or more processors. Examples of processors includemicroprocessors, microcontrollers, graphics processing units (GPUs),central processing units (CPUs), application processors, digital signalprocessors (DSPs), reduced instruction set computing (RISC) processors,systems on a chip (SoC), baseband processors, field programmable gatearrays (FPGAs), programmable logic devices (PLDs), state machines, gatedlogic, discrete hardware circuits, and other suitable hardwareconfigured to perform the various functionality described throughoutthis disclosure. One or more processors in the processing system mayexecute software. Software, whether referred to as software, firmware,middleware, microcode, hardware description language, or otherwise,shall be construed broadly to mean instructions, instruction sets, code,code segments, program code, programs, subprograms, software components,applications, software applications, software packages, routines,subroutines, objects, executables, threads of execution, procedures,functions, or any combination thereof.

Accordingly, in one or more example aspects, implementations, and/or usecases, the functions described may be implemented in hardware, software,or any combination thereof. If implemented in software, the functionsmay be stored on or encoded as one or more instructions or code on acomputer-readable medium. Computer-readable media includes computerstorage media. Storage media may be any available media that can beaccessed by a computer. By way of example, such computer-readable mediacan comprise a random-access memory (RAM), a read-only memory (ROM), anelectrically erasable programmable ROM (EEPROM), optical disk storage,magnetic disk storage, other magnetic storage devices, combinations ofthe types of computer-readable media, or any other medium that can beused to store computer executable code in the form of instructions ordata structures that can be accessed by a computer.

While aspects, implementations, and/or use cases are described in thisapplication by illustration to some examples, additional or differentaspects, implementations and/or use cases may come about in manydifferent arrangements and scenarios. Aspects, implementations, and/oruse cases described herein may be implemented across many differingplatform types, devices, systems, shapes, sizes, and packagingarrangements. For example, aspects, implementations, and/or use casesmay come about via integrated chip implementations and othernon-module-component based devices (e.g., end-user devices, vehicles,communication devices, computing devices, industrial equipment,retail/purchasing devices, medical devices, artificial intelligence(AI)-enabled devices, etc.). While some examples may or may not bespecifically directed to use cases or applications, a wide assortment ofapplicability of described examples may occur. Aspects, implementations,and/or use cases may range a spectrum from chip-level or modularcomponents to non-modular, non-chip-level implementations and further toaggregate, distributed, or original equipment manufacturer (OEM) devicesor systems incorporating one or more techniques herein. In somepractical settings, devices incorporating described aspects and featuresmay also include additional components and features for implementationand practice of claimed and described aspect. For example, transmissionand reception of wireless signals necessarily includes a number ofcomponents for analog and digital purposes (e.g., hardware componentsincluding antenna, RF-chains, power amplifiers, modulators, buffer,processor(s), interleaver, adders/summers, etc.). Techniques describedherein may be practiced in a wide variety of devices, chip-levelcomponents, systems, distributed arrangements, aggregated ordisaggregated components, end-user devices, etc. of varying sizes,shapes, and constitution.

Deployment of communication systems, such as 5G NR systems, may bearranged in multiple manners with various components or constituentparts. In a 5G NR system, or network, a network node, a network entity,a mobility element of a network, a radio access network (RAN) node, acore network node, a network element, or a network equipment, such as abase station (BS), or one or more units (or one or more components)performing base station functionality, may be implemented in anaggregated or disaggregated architecture. For example, a BS (such as aNode B (NB), evolved NB (eNB), NR BS, 5G NB, access point (AP), atransmit receive point (TRP), or a cell, etc.) may be implemented as anaggregated base station (also known as a standalone BS or a monolithicBS) or a disaggregated base station.

An aggregated base station may be configured to utilize a radio protocolstack that is physically or logically integrated within a single RANnode. A disaggregated base station may be configured to utilize aprotocol stack that is physically or logically distributed among two ormore units (such as one or more central or centralized units (CUs), oneor more distributed units (DUs), or one or more radio units (RUs)). Insome aspects, a CU may be implemented within a RAN node, and one or moreDUs may be co-located with the CU, or alternatively, may begeographically or virtually distributed throughout one or multiple otherRAN nodes. The DUs may be implemented to communicate with one or moreRUs. Each of the CU, DU and RU can be implemented as virtual units,i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), ora virtual radio unit.

Base station operation or network design may consider aggregationcharacteristics of base station functionality. For example,disaggregated base stations may be utilized in an integrated accessbackhaul (IAB) network, an open radio access network (O-RAN (such as thenetwork configuration sponsored by the O-RAN Alliance)), or avirtualized radio access network (vRAN, also known as a cloud radioaccess network (C-RAN)). Disaggregation may include distributingfunctionality across two or more units at various physical locations, aswell as distributing functionality for at least one unit virtually,which can enable flexibility in network design. The various units of thedisaggregated base station, or disaggregated RAN architecture, can beconfigured for wired or wireless communication with at least one otherunit.

FIG. 1 is a diagram 100 illustrating an example of a wirelesscommunications system and an access network. The illustrated wirelesscommunications system includes a disaggregated base stationarchitecture. The disaggregated base station architecture may includeone or more CUs 110 that can communicate directly with a core network120 via a backhaul link, or indirectly with the core network 120 throughone or more disaggregated base station units (such as a Near-Real Time(Near-RT) RAN Intelligent Controller (MC) 125 via an E2 link, or aNon-Real Time (Non-RT) RIC 115 associated with a Service Management andOrchestration (SMO) Framework 105, or both). A CU 110 may communicatewith one or more DUs 130 via respective midhaul links, such as an F1interface. The DUs 130 may communicate with one or more RUs 140 viarespective fronthaul links. The RUs 140 may communicate with respectiveUEs 104 via one or more radio frequency (RF) access links. In someimplementations, the UE 104 may be simultaneously served by multiple RUs140.

Each of the units, i.e., the CUs 110, the DUs 130, the RUs 140, as wellas the Near-RT RICs 125, the Non-RT RICs 115, and the SMO Framework 105,may include one or more interfaces or be coupled to one or moreinterfaces configured to receive or to transmit signals, data, orinformation (collectively, signals) via a wired or wireless transmissionmedium. Each of the units, or an associated processor or controllerproviding instructions to the communication interfaces of the units, canbe configured to communicate with one or more of the other units via thetransmission medium. For example, the units can include a wiredinterface configured to receive or to transmit signals over a wiredtransmission medium to one or more of the other units. Additionally, theunits can include a wireless interface, which may include a receiver, atransmitter, or a transceiver (such as an RF transceiver), configured toreceive or to transmit signals, or both, over a wireless transmissionmedium to one or more of the other units.

In some aspects, the CU 110 may host one or more higher layer controlfunctions. Such control functions can include radio resource control(RRC), packet data convergence protocol (PDCP), service data adaptationprotocol (SDAP), or the like. Each control function can be implementedwith an interface configured to communicate signals with other controlfunctions hosted by the CU 110. The CU 110 may be configured to handleuser plane functionality (i.e., Central Unit-User Plane (CU-UP)),control plane functionality (i.e., Central Unit-Control Plane (CU-CP)),or a combination thereof. In some implementations, the CU 110 can belogically split into one or more CU-UP units and one or more CU-CPunits. The CU-UP unit can communicate bidirectionally with the CU-CPunit via an interface, such as an E1 interface when implemented in anO-RAN configuration. The CU 110 can be implemented to communicate withthe DU 130, as necessary, for network control and signaling.

The DU 130 may correspond to a logical unit that includes one or morebase station functions to control the operation of one or more RUs 140.In some aspects, the DU 130 may host one or more of a radio link control(RLC) layer, a medium access control (MAC) layer, and one or more highphysical (PHY) layers (such as modules for forward error correction(FEC) encoding and decoding, scrambling, modulation, demodulation, orthe like) depending, at least in part, on a functional split, such asthose defined by 3GPP. In some aspects, the DU 130 may further host oneor more low PHY layers. Each layer (or module) can be implemented withan interface configured to communicate signals with other layers (andmodules) hosted by the DU 130, or with the control functions hosted bythe CU 110.

Lower-layer functionality can be implemented by one or more RUs 140. Insome deployments, an RU 140, controlled by a DU 130, may correspond to alogical node that hosts RF processing functions, or low-PHY layerfunctions (such as performing fast Fourier transform (FFT), inverse FFT(iFFT), digital beamforming, physical random access channel (PRACH)extraction and filtering, or the like), or both, based at least in parton the functional split, such as a lower layer functional split. In suchan architecture, the RU(s) 140 can be implemented to handle over the air(OTA) communication with one or more UEs 104. In some implementations,real-time and non-real-time aspects of control and user planecommunication with the RU(s) 140 can be controlled by the correspondingDU 130. In some scenarios, this configuration can enable the DU(s) 130and the CU 110 to be implemented in a cloud-based RAN architecture, suchas a vRAN architecture.

The SMO Framework 105 may be configured to support RAN deployment andprovisioning of non-virtualized and virtualized network elements. Fornon-virtualized network elements, the SMO Framework 105 may beconfigured to support the deployment of dedicated physical resources forRAN coverage requirements that may be managed via an operations andmaintenance interface (such as an O1 interface). For virtualized networkelements, the SMO Framework 105 may be configured to interact with acloud computing platform (such as an open cloud (O-Cloud) 190) toperform network element life cycle management (such as to instantiatevirtualized network elements) via a cloud computing platform interface(such as an O2 interface).

Such virtualized network elements can include, but are not limited to,CUs 110, DUs 130, RUs 140 and Near-RT RICs 125. In some implementations,the SMO Framework 105 can communicate with a hardware aspect of a 4GRAN, such as an open eNB (O-eNB) 111, via an O1 interface. Additionally,in some implementations, the SMO Framework 105 can communicate directlywith one or more RUs 140 via an O1 interface. The SMO Framework 105 alsomay include a Non-RT RIC 115 configured to support functionality of theSMO Framework 105.

The Non-RT RIC 115 may be configured to include a logical function thatenables non-real-time control and optimization of RAN elements andresources, artificial intelligence (AI)/machine learning (ML) (AI/ML)workflows including model training and updates, or policy-based guidanceof applications/features in the Near-RT RIC 125. The Non-RT RIC 115 maybe coupled to or communicate with (such as via an A1 interface) theNear-RT RIC 125. The Near-RT RIC 125 may be configured to include alogical function that enables near-real-time control and optimization ofRAN elements and resources via data collection and actions over aninterface (such as via an E2 interface) connecting one or more CUs 110,one or more DUs 130, or both, as well as an O-eNB, with the Near-RT RIC125.

In some implementations, to generate AI/ML models to be deployed in theNear-RT RIC 125, the Non-RT RIC 115 may receive parameters or externalenrichment information from external servers. Such information may beutilized by the Near-RT RIC 125 and may be received at the SMO Framework105 or the Non-RT RIC 115 from non-network data sources or from networkfunctions. In some examples, the Non-RT RIC 115 or the Near-RT RIC 125may be configured to tune RAN behavior or performance. For example, theNon-RT RIC 115 may monitor long-term trends and patterns for performanceand employ AI/ML models to perform corrective actions through the SMOFramework 105 (such as reconfiguration via 01) or via creation of RANmanagement policies (such as A1 policies).

At least one of the CU 110, the DU 130, and the RU 140 may be referredto as a base station 102. Accordingly, a base station 102 may includeone or more of the CU 110, the DU 130, and the RU 140 (each componentindicated with dotted lines to signify that each component may or maynot be included in the base station 102). The base station 102 providesan access point to the core network 120 for a UE 104. The base stations102 may include macrocells (high power cellular base station) and/orsmall cells (low power cellular base station). The small cells includefemtocells, picocells, and microcells. A network that includes bothsmall cell and macrocells may be known as a heterogeneous network. Aheterogeneous network may also include Home Evolved Node Bs (eNBs)(HeNBs), which may provide service to a restricted group known as aclosed subscriber group (CSG). The communication links between the RUs140 and the UEs 104 may include uplink (UL) (also referred to as reverselink) transmissions from a UE 104 to an RU 140 and/or downlink (DL)(also referred to as forward link) transmissions from an RU 140 to a UE104. The communication links may use multiple-input and multiple-output(MIMO) antenna technology, including spatial multiplexing, beamforming,and/or transmit diversity. The communication links may be through one ormore carriers. The base stations 102/UEs 104 may use spectrum up to YMHz (e.g., 5, 10, 15, 20, 100, 400, etc. MHz) bandwidth per carrierallocated in a carrier aggregation of up to a total of Yx MHz (xcomponent carriers) used for transmission in each direction. Thecarriers may or may not be adjacent to each other. Allocation ofcarriers may be asymmetric with respect to DL and UL (e.g., more orfewer carriers may be allocated for DL than for UL). The componentcarriers may include a primary component carrier and one or moresecondary component carriers. A primary component carrier may bereferred to as a primary cell (PCell) and a secondary component carriermay be referred to as a secondary cell (SCell).

Certain UEs 104 may communicate with each other using device-to-device(D2D) communication link 158. The D2D communication link 158 may use theDL/UL wireless wide area network (WWAN) spectrum. The D2D communicationlink 158 may use one or more sidelink channels, such as a physicalsidelink broadcast channel (PSBCH), a physical sidelink discoverychannel (PSDCH), a physical sidelink shared channel (PSSCH), and aphysical sidelink control channel (PSCCH). D2D communication may bethrough a variety of wireless D2D communications systems, such as forexample, Bluetooth, Wi-Fi based on the Institute of Electrical andElectronics Engineers (IEEE) 802.11 standard, LTE, or NR.

The wireless communications system may further include a Wi-Fi AP 150 incommunication with UEs 104 (also referred to as Wi-Fi stations (STAs))via communication link 154, e.g., in a 5 GHz unlicensed frequencyspectrum or the like. When communicating in an unlicensed frequencyspectrum, the UEs 104/AP 150 may perform a clear channel assessment(CCA) prior to communicating in order to determine whether the channelis available.

The electromagnetic spectrum is often subdivided, based onfrequency/wavelength, into various classes, bands, channels, etc. In 5GNR, two initial operating bands have been identified as frequency rangedesignations FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz).Although a portion of FR1 is greater than 6 GHz, FR1 is often referredto (interchangeably) as a “sub-6 GHz” band in various documents andarticles. A similar nomenclature issue sometimes occurs with regard toFR2, which is often referred to (interchangeably) as a “millimeter wave”band in documents and articles, despite being different from theextremely high frequency (EHF) band (30 GHz-300 GHz) which is identifiedby the International Telecommunications Union (ITU) as a “millimeterwave” band.

The frequencies between FR1 and FR2 are often referred to as mid-bandfrequencies. Recent 5G NR studies have identified an operating band forthese mid-band frequencies as frequency range designation FR3 (7.125GHz-24.25 GHz). Frequency bands falling within FR3 may inherit FR1characteristics and/or FR2 characteristics, and thus may effectivelyextend features of FR1 and/or FR2 into mid-band frequencies. Inaddition, higher frequency bands are currently being explored to extend5G NR operation beyond 52.6 GHz. For example, three higher operatingbands have been identified as frequency range designations FR2-2 (52.6GHz-71 GHz), FR4 (71 GHz-114.25 GHz), and FR5 (114.25 GHz-300 GHz). Eachof these higher frequency bands falls within the EHF band.

With the above aspects in mind, unless specifically stated otherwise,the term “sub-6 GHz” or the like if used herein may broadly representfrequencies that may be less than 6 GHz, may be within FR1, or mayinclude mid-band frequencies. Further, unless specifically statedotherwise, the term “millimeter wave” or the like if used herein maybroadly represent frequencies that may include mid-band frequencies, maybe within FR2, FR4, FR2-2, and/or FR5, or may be within the EHF band.

The base station 102 and the UE 104 may each include a plurality ofantennas, such as antenna elements, antenna panels, and/or antennaarrays to facilitate beamforming. The base station 102 may transmit abeamformed signal 182 to the UE 104 in one or more transmit directions.The UE 104 may receive the beamformed signal from the base station 102in one or more receive directions. The UE 104 may also transmit abeamformed signal 184 to the base station 102 in one or more transmitdirections. The base station 102 may receive the beamformed signal fromthe UE 104 in one or more receive directions. The base station 102/UE104 may perform beam training to determine the best receive and transmitdirections for each of the base station 102/UE 104. The transmit andreceive directions for the base station 102 may or may not be the same.The transmit and receive directions for the UE 104 may or may not be thesame.

The base station 102 may include and/or be referred to as a gNB, Node B,eNB, an access point, a base transceiver station, a radio base station,a radio transceiver, a transceiver function, a basic service set (BSS),an extended service set (ESS), a transmit reception point (TRP), networknode, network entity, network equipment, or some other suitableterminology. The base station 102 can be implemented as an integratedaccess and backhaul (IAB) node, a relay node, a sidelink node, anaggregated (monolithic) base station with a baseband unit (BBU)(including a CU and a DU) and an RU, or as a disaggregated base stationincluding one or more of a CU, a DU, and/or an RU. The set of basestations, which may include disaggregated base stations and/oraggregated base stations, may be referred to as next generation (NG) RAN(NG-RAN).

The core network 120 may include an Access and Mobility ManagementFunction (AMF) 161, a Session Management Function (SMF) 162, a UserPlane Function (UPF) 163, a Unified Data Management (UDM) 164, one ormore location servers 168, and other functional entities. The AMF 161 isthe control node that processes the signaling between the UEs 104 andthe core network 120. The AMF 161 supports registration management,connection management, mobility management, and other functions. The SMF162 supports session management and other functions. The UPF 163supports packet routing, packet forwarding, and other functions. The UDM164 supports the generation of authentication and key agreement (AKA)credentials, user identification handling, access authorization, andsubscription management. The one or more location servers 168 areillustrated as including a Gateway Mobile Location Center (GMLC) 165 anda Location Management Function (LMF) 166. However, generally, the one ormore location servers 168 may include one or more location/positioningservers, which may include one or more of the GMLC 165, the LMF 166, aposition determination entity (PDE), a serving mobile location center(SMLC), a mobile positioning center (MPC), or the like. The GMLC 165 andthe LMF 166 support UE location services. The GMLC 165 provides aninterface for clients/applications (e.g., emergency services) foraccessing UE positioning information. The LMF 166 receives measurementsand assistance information from the NG-RAN and the UE 104 via the AMF161 to compute the position of the UE 104. The NG-RAN may utilize one ormore positioning methods in order to determine the position of the UE104. Positioning the UE 104 may involve signal measurements, a positionestimate, and an optional velocity computation based on themeasurements. The signal measurements may be made by the UE 104 and/orthe serving base station 102. The signals measured may be based on oneor more of a satellite positioning system (SPS) 170 (e.g., one or moreof a Global Navigation Satellite System (GNSS), global position system(GPS), non-terrestrial network (NTN), or other satelliteposition/location system), LTE signals, wireless local area network(WLAN) signals, Bluetooth signals, a terrestrial beacon system (TBS),sensor-based information (e.g., barometric pressure sensor, motionsensor), NR enhanced cell ID (NR E-CID) methods, NR signals (e.g.,multi-round trip time (Multi-RTT), DL angle-of-departure (DL-AoD), DLtime difference of arrival (DL-TDOA), UL time difference of arrival(UL-TDOA), and UL angle-of-arrival (UL-AoA) positioning), and/or othersystems/signals/sensors.

Examples of UEs 104 include a cellular phone, a smart phone, a sessioninitiation protocol (SIP) phone, a laptop, a personal digital assistant(PDA), a satellite radio, a global positioning system, a multimediadevice, a video device, a digital audio player (e.g., MP3 player), acamera, a game console, a tablet, a smart device, a wearable device, avehicle, an electric meter, a gas pump, a large or small kitchenappliance, a healthcare device, an implant, a sensor/actuator, adisplay, or any other similar functioning device. Some of the UEs 104may be referred to as IoT devices (e.g., parking meter, gas pump,toaster, vehicles, heart monitor, etc.). The UE 104 may also be referredto as a station, a mobile station, a subscriber station, a mobile unit,a subscriber unit, a wireless unit, a remote unit, a mobile device, awireless device, a wireless communications device, a remote device, amobile subscriber station, an access terminal, a mobile terminal, awireless terminal, a remote terminal, a handset, a user agent, a mobileclient, a client, or some other suitable terminology. In some scenarios,the term UE may also apply to one or more companion devices such as in adevice constellation arrangement. One or more of these devices maycollectively access the network and/or individually access the network.

Referring again to FIG. 1 , in certain aspects, a device, which may be aUE 104, a base station 102/180, a component of the base station 102/180,or a location server (e.g., the GMLC 165, the LMF 166), may include apositioning ML component 198/199 configured to perform a position-gridbased ML or a position-grid based positioning to improve the accuracy ofidentifying a warm-start position of the device. In one configuration,the positioning ML component 198/199 may be configured to determine, foreach grid point within a range of an initial position of a UE, a set ofPR residuals based on PRs for each SV of a set of SVs. In suchconfiguration, the positioning ML component 198/199 may determine anestimated position of the UE based on the sets of determined PRresiduals.

FIG. 2A is a diagram 200 illustrating an example of a first subframewithin a 5G NR frame structure. FIG. 2B is a diagram 230 illustrating anexample of DL channels within a 5G NR subframe. FIG. 2C is a diagram 250illustrating an example of a second subframe within a 5G NR framestructure. FIG. 2D is a diagram 280 illustrating an example of ULchannels within a 5G NR subframe. The 5G NR frame structure may befrequency division duplexed (FDD) in which for a particular set ofsubcarriers (carrier system bandwidth), subframes within the set ofsubcarriers are dedicated for either DL or UL, or may be time divisionduplexed (TDD) in which for a particular set of subcarriers (carriersystem bandwidth), subframes within the set of subcarriers are dedicatedfor both DL and UL. In the examples provided by FIGS. 2A, 2C, the 5G NRframe structure is assumed to be TDD, with subframe 4 being configuredwith slot format 28 (with mostly DL), where D is DL, U is UL, and F isflexible for use between DL/UL, and subframe 3 being configured withslot format 1 (with all UL). While subframes 3, 4 are shown with slotformats 1, 28, respectively, any particular subframe may be configuredwith any of the various available slot formats 0-61. Slot formats 0, 1are all DL, UL, respectively. Other slot formats 2-61 include a mix ofDL, UL, and flexible symbols. UEs are configured with the slot format(dynamically through DL control information (DCI), orsemi-statically/statically through radio resource control (RRC)signaling) through a received slot format indicator (SFI). Note that thedescription infra applies also to a 5G NR frame structure that is TDD.

FIGS. 2A-2D illustrate a frame structure, and the aspects of the presentdisclosure may be applicable to other wireless communicationtechnologies, which may have a different frame structure and/ordifferent channels. A frame (10 ms) may be divided into 10 equally sizedsubframes (1 ms). Each subframe may include one or more time slots.Subframes may also include mini-slots, which may include 7, 4, or 2symbols. Each slot may include 14 or 12 symbols, depending on whetherthe cyclic prefix (CP) is normal or extended. For normal CP, each slotmay include 14 symbols, and for extended CP, each slot may include 12symbols. The symbols on DL may be CP orthogonal frequency divisionmultiplexing (OFDM) (CP-OFDM) symbols. The symbols on UL may be CP-OFDMsymbols (for high throughput scenarios) or discrete Fourier transform(DFT) spread OFDM (DFT-s-OFDM) symbols (also referred to as singlecarrier frequency-division multiple access (SC-FDMA) symbols) (for powerlimited scenarios; limited to a single stream transmission). The numberof slots within a subframe is based on the CP and the numerology. Thenumerology defines the subcarrier spacing (SCS) and, effectively, thesymbol length/duration, which is equal to 1/SCS.

SCS μ Δf = 2^(μ) · 15[kHz] Cyclic prefix 0 15 Normal 1 30 Normal 2 60Normal, Extended 3 120 Normal 4 240 Normal

For normal CP (14 symbols/slot), different numerologies μ 0 to 4 allowfor 1, 2, 4, 8, and 16 slots, respectively, per subframe. For extendedCP, the numerology 2 allows for 4 slots per subframe. Accordingly, fornormal CP and numerology μ, there are 14 symbols/slot and 2μslots/subframe. The subcarrier spacing may be equal to 2^(μ)*15 kHz,where μ is the numerology 0 to 4. As such, the numerology μ=0 has asubcarrier spacing of 15 kHz and the numerology μ=4 has a subcarrierspacing of 240 kHz. The symbol length/duration is inversely related tothe subcarrier spacing. FIGS. 2A-2D provide an example of normal CP with14 symbols per slot and numerology μ=2 with 4 slots per subframe. Theslot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and thesymbol duration is approximately 16.67 μs. Within a set of frames, theremay be one or more different bandwidth parts (BWPs) (see FIG. 2B) thatare frequency division multiplexed. Each BWP may have a particularnumerology and CP (normal or extended).

A resource grid may be used to represent the frame structure. Each timeslot includes a resource block (RB) (also referred to as physical RBs(PRBs)) that extends 12 consecutive subcarriers. The resource grid isdivided into multiple resource elements (REs). The number of bitscarried by each RE depends on the modulation scheme.

As illustrated in FIG. 2A, some of the REs carry reference (pilot)signals (RS) for the UE. The RS may include demodulation RS (DM-RS)(indicated as R for one particular configuration, but other DM-RSconfigurations are possible) and channel state information referencesignals (CSI-RS) for channel estimation at the UE. The RS may alsoinclude beam measurement RS (BRS), beam refinement RS (BRRS), and phasetracking RS (PT-RS).

FIG. 2B illustrates an example of various DL channels within a subframeof a frame. The physical downlink control channel (PDCCH) carries DCIwithin one or more control channel elements (CCEs) (e.g., 1, 2, 4, 8, or16 CCEs), each CCE including six RE groups (REGs), each REG including 12consecutive REs in an OFDM symbol of an RB. A PDCCH within one BWP maybe referred to as a control resource set (CORESET). A UE is configuredto monitor PDCCH candidates in a PDCCH search space (e.g., common searchspace, UE-specific search space) during PDCCH monitoring occasions onthe CORESET, where the PDCCH candidates have different DCI formats anddifferent aggregation levels. Additional BWPs may be located at greaterand/or lower frequencies across the channel bandwidth. A primarysynchronization signal (PSS) may be within symbol 2 of particularsubframes of a frame. The PSS is used by a UE 104 to determinesubframe/symbol timing and a physical layer identity. A secondarysynchronization signal (SSS) may be within symbol 4 of particularsubframes of a frame. The SSS is used by a UE to determine a physicallayer cell identity group number and radio frame timing. Based on thephysical layer identity and the physical layer cell identity groupnumber, the UE can determine a physical cell identifier (PCI). Based onthe PCI, the UE can determine the locations of the DM-RS. The physicalbroadcast channel (PBCH), which carries a master information block(MIB), may be logically grouped with the PSS and SSS to form asynchronization signal (SS)/PBCH block (also referred to as SS block(SSB)). The MIB provides a number of RBs in the system bandwidth and asystem frame number (SFN). The physical downlink shared channel (PDSCH)carries user data, broadcast system information not transmitted throughthe PBCH such as system information blocks (SIBs), and paging messages.

As illustrated in FIG. 2C, some of the REs carry DM-RS (indicated as Rfor one particular configuration, but other DM-RS configurations arepossible) for channel estimation at the base station. The UE maytransmit DM-RS for the physical uplink control channel (PUCCH) and DM-RSfor the physical uplink shared channel (PUSCH). The PUSCH DM-RS may betransmitted in the first one or two symbols of the PUSCH. The PUCCHDM-RS may be transmitted in different configurations depending onwhether short or long PUCCHs are transmitted and depending on theparticular PUCCH format used. The UE may transmit sounding referencesignals (SRS). The SRS may be transmitted in the last symbol of asubframe. The SRS may have a comb structure, and a UE may transmit SRSon one of the combs. The SRS may be used by a base station for channelquality estimation to enable frequency-dependent scheduling on the UL.

FIG. 2D illustrates an example of various UL channels within a subframeof a frame. The PUCCH may be located as indicated in one configuration.The PUCCH carries uplink control information (UCI), such as schedulingrequests, a channel quality indicator (CQI), a precoding matrixindicator (PMI), a rank indicator (RI), and hybrid automatic repeatrequest (HARQ) acknowledgment (ACK) (HARQ-ACK) feedback (i.e., one ormore HARQ ACK bits indicating one or more ACK and/or negative ACK(NACK)). The PUSCH carries data, and may additionally be used to carry abuffer status report (BSR), a power headroom report (PHR), and/or UCI.

FIG. 3 is a block diagram of a base station 310 in communication with aUE 350 in an access network. In the DL, Internet protocol (IP) packetsmay be provided to a controller/processor 375. The controller/processor375 implements layer 3 and layer 2 functionality. Layer 3 includes aradio resource control (RRC) layer, and layer 2 includes a service dataadaptation protocol (SDAP) layer, a packet data convergence protocol(PDCP) layer, a radio link control (RLC) layer, and a medium accesscontrol (MAC) layer. The controller/processor 375 provides RRC layerfunctionality associated with broadcasting of system information (e.g.,MIB, SIBs), RRC connection control (e.g., RRC connection paging, RRCconnection establishment, RRC connection modification, and RRCconnection release), inter radio access technology (RAT) mobility, andmeasurement configuration for UE measurement reporting; PDCP layerfunctionality associated with header compression/decompression, security(ciphering, deciphering, integrity protection, integrity verification),and handover support functions; RLC layer functionality associated withthe transfer of upper layer packet data units (PDUs), error correctionthrough ARQ, concatenation, segmentation, and reassembly of RLC servicedata units (SDUs), re-segmentation of RLC data PDUs, and reordering ofRLC data PDUs; and MAC layer functionality associated with mappingbetween logical channels and transport channels, multiplexing of MACSDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs,scheduling information reporting, error correction through HARQ,priority handling, and logical channel prioritization.

The transmit (TX) processor 316 and the receive (RX) processor 370implement layer 1 functionality associated with various signalprocessing functions. Layer 1, which includes a physical (PHY) layer,may include error detection on the transport channels, forward errorcorrection (FEC) coding/decoding of the transport channels,interleaving, rate matching, mapping onto physical channels,modulation/demodulation of physical channels, and MIMO antennaprocessing. The TX processor 316 handles mapping to signalconstellations based on various modulation schemes (e.g., binaryphase-shift keying (BPSK), quadrature phase-shift keying (QPSK),M-phase-shift keying (M-PSK), M-quadrature amplitude modulation(M-QAM)). The coded and modulated symbols may then be split intoparallel streams. Each stream may then be mapped to an OFDM subcarrier,multiplexed with a reference signal (e.g., pilot) in the time and/orfrequency domain, and then combined together using an Inverse FastFourier Transform (IFFT) to produce a physical channel carrying a timedomain OFDM symbol stream. The OFDM stream is spatially precoded toproduce multiple spatial streams. Channel estimates from a channelestimator 374 may be used to determine the coding and modulation scheme,as well as for spatial processing. The channel estimate may be derivedfrom a reference signal and/or channel condition feedback transmitted bythe UE 350. Each spatial stream may then be provided to a differentantenna 320 via a separate transmitter 318Tx. Each transmitter 318Tx maymodulate a radio frequency (RF) carrier with a respective spatial streamfor transmission.

At the UE 350, each receiver 354Rx receives a signal through itsrespective antenna 352. Each receiver 354Rx recovers informationmodulated onto an RF carrier and provides the information to the receive(RX) processor 356. The TX processor 368 and the RX processor 356implement layer 1 functionality associated with various signalprocessing functions. The RX processor 356 may perform spatialprocessing on the information to recover any spatial streams destinedfor the UE 350. If multiple spatial streams are destined for the UE 350,they may be combined by the RX processor 356 into a single OFDM symbolstream. The RX processor 356 then converts the OFDM symbol stream fromthe time-domain to the frequency domain using a Fast Fourier Transform(FFT). The frequency domain signal comprises a separate OFDM symbolstream for each subcarrier of the OFDM signal. The symbols on eachsubcarrier, and the reference signal, are recovered and demodulated bydetermining the most likely signal constellation points transmitted bythe base station 310. These soft decisions may be based on channelestimates computed by the channel estimator 358. The soft decisions arethen decoded and deinterleaved to recover the data and control signalsthat were originally transmitted by the base station 310 on the physicalchannel. The data and control signals are then provided to thecontroller/processor 359, which implements layer 3 and layer 2functionality.

The controller/processor 359 can be associated with a memory 360 thatstores program codes and data. The memory 360 may be referred to as acomputer-readable medium. In the UL, the controller/processor 359provides demultiplexing between transport and logical channels, packetreassembly, deciphering, header decompression, and control signalprocessing to recover IP packets. The controller/processor 359 is alsoresponsible for error detection using an ACK and/or NACK protocol tosupport HARQ operations.

Similar to the functionality described in connection with the DLtransmission by the base station 310, the controller/processor 359provides RRC layer functionality associated with system information(e.g., MIB, SIBs) acquisition, RRC connections, and measurementreporting; PDCP layer functionality associated with headercompression/decompression, and security (ciphering, deciphering,integrity protection, integrity verification); RLC layer functionalityassociated with the transfer of upper layer PDUs, error correctionthrough ARQ, concatenation, segmentation, and reassembly of RLC SDUs,re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; andMAC layer functionality associated with mapping between logical channelsand transport channels, multiplexing of MAC SDUs onto TBs,demultiplexing of MAC SDUs from TBs, scheduling information reporting,error correction through HARQ, priority handling, and logical channelprioritization.

Channel estimates derived by a channel estimator 358 from a referencesignal or feedback transmitted by the base station 310 may be used bythe TX processor 368 to select the appropriate coding and modulationschemes, and to facilitate spatial processing. The spatial streamsgenerated by the TX processor 368 may be provided to different antenna352 via separate transmitters 354Tx. Each transmitter 354Tx may modulatean RF carrier with a respective spatial stream for transmission.

The UL transmission is processed at the base station 310 in a mannersimilar to that described in connection with the receiver function atthe UE 350. Each receiver 318Rx receives a signal through its respectiveantenna 320. Each receiver 318Rx recovers information modulated onto anRF carrier and provides the information to a RX processor 370.

The controller/processor 375 can be associated with a memory 376 thatstores program codes and data. The memory 376 may be referred to as acomputer-readable medium. In the UL, the controller/processor 375provides demultiplexing between transport and logical channels, packetreassembly, deciphering, header decompression, control signal processingto recover IP packets. The controller/processor 375 is also responsiblefor error detection using an ACK and/or NACK protocol to support HARQoperations.

At least one of the TX processor 368, the RX processor 356, and thecontroller/processor 359 may be configured to perform aspects inconnection with the positioning ML component 198 of FIG. 1 .

At least one of the TX processor 316, the RX processor 370, and thecontroller/processor 375 may be configured to perform aspects inconnection with the positioning ML component 199 of FIG. 1 .

FIG. 4 is a diagram 400 illustrating an example of a UE positioningbased on reference signal measurements (which may also be referred to as“network based positioning”) in accordance with various aspects of thepresent disclosure. The UE 404 may transmit UL-SRS 412 at timeT_(SRS_TX) and receive DL positioning reference signals (PRS) (DL-PRS)410 at time T_(PRS_RX). The TRP 406 may receive the UL-SRS 412 at timeT_(SRS_RX) and transmit the DL-PRS 410 at time T_(PRS_TX). The UE 404may receive the DL-PRS 410 before transmitting the UL-SRS 412, or maytransmit the UL-SRS 412 before receiving the DL-PRS 410. In both cases,a positioning server (e.g., location server(s)168) or the UE 404 maydetermine the RTT 414 based on∥T_(SRS_RX)−T_(PRS_TX)|−|T_(SRS_TX)−T_(PRS_RX)∥. Accordingly, multi-RTTpositioning may make use of the UE Rx-Tx time difference measurements(i.e., |T_(SRS_TX)−T_(PRS_RX)|) and DL-PRS reference signal receivedpower (RSRP) (DL-PRS-RSRP) of downlink signals received from multipleTRPs 402, 406 and measured by the UE 404, and the measured TRP Rx-Txtime difference measurements (i.e., |T_(SRS_RX)−T_(PRS_TX)|) andUL-SRS-RSRP at multiple TRPs 402, 406 of uplink signals transmitted fromUE 404. The UE 404 measures the UE Rx-Tx time difference measurements(and optionally DL-PRS-RSRP of the received signals) using assistancedata received from the positioning server, and the TRPs 402, 406 measurethe gNB Rx-Tx time difference measurements (and optionally UL-SRS-RSRPof the received signals) using assistance data received from thepositioning server. The measurements may be used at the positioningserver or the UE 404 to determine the RTT, which is used to estimate thelocation of the UE 404. Other methods are possible for determining theRTT, such as for example using DL-TDOA and/or UL-TDOA measurements.

DL-AoD positioning may make use of the measured DL-PRS-RSRP of downlinksignals received from multiple TRPs 402, 406 at the UE 404. The UE 404measures the DL-PRS-RSRP of the received signals using assistance datareceived from the positioning server, and the resulting measurements areused along with the azimuth angle of departure (A-AoD), the zenith angleof departure (Z-AoD), and other configuration information to locate theUE 404 in relation to the neighboring TRPs 402, 406.

DL-TDOA positioning may make use of the DL reference signal timedifference (RSTD) (and optionally DL-PRS-RSRP) of downlink signalsreceived from multiple TRPs 402, 406 at the UE 404. The UE 404 measuresthe DL RSTD (and optionally DL-PRS-RSRP) of the received signals usingassistance data received from the positioning server, and the resultingmeasurements are used along with other configuration information tolocate the UE 404 in relation to the neighboring TRPs 402, 406.

UL-TDOA positioning may make use of the UL relative time of arrival(RTOA) (and optionally UL-SRS-RSRP) at multiple TRPs 402, 406 of uplinksignals transmitted from UE 404. The TRPs 402, 406 measure the UL-RTOA(and optionally UL-SRS-RSRP) of the received signals using assistancedata received from the positioning server, and the resultingmeasurements are used along with other configuration information toestimate the location of the UE 404.

UL-AoA positioning may make use of the measured azimuth angle of arrival(A-AoA) and zenith angle of arrival (Z-AoA) at multiple TRPs 402, 406 ofuplink signals transmitted from the UE 404. The TRPs 402, 406 measurethe A-AoA and the Z-AoA of the received signals using assistance datareceived from the positioning server, and the resulting measurements areused along with other configuration information to estimate the locationof the UE 404.

Additional positioning methods may be used for estimating the locationof the UE 404, such as for example, UE-side UL-AoD and/or DL-AoA. Notethat data/measurements from various technologies may be combined invarious ways to increase accuracy, to determine and/or to enhancecertainty, to supplement/complement measurements, and/or tosubstitute/provide for missing information.

A device equipped with a global navigation satellite system (GNSS)receiver may determine its location based on GNSS positioning. GNSS is anetwork of satellite s broadcasting timing and orbital information usedfor navigation and positioning measurements. GNSS may include multiplegroups of satellites, known as constellations, that broadcast signals(which may be referred to as GNSS signals) to control stations and usersof GNSS. Based on the broadcast signals, the users may be able todetermine their locations (e.g., via trilateration). For purposes of thepresent disclosure, a device (e.g., a UE) that is equipped with a GNSSreceiver or is capable of receiving GNSS signals may be referred to as aGNSS device, and a device that is capable of transmitting GNSS signals,such as a satellite, may be referred to as a space vehicle (SV).

FIG. 5 is a diagram 500 illustrating an example of GNSS positioning inaccordance with various aspects of the present disclosure. A GNSS device506 may calculate its position and time based at least in part on data(e.g., GNSS signals 504) received from multiple space vehicles (SVs)502, where each SV 502 may carry a record of its position and time andmay transmit that data (e.g., the record) to the GNSS device 506. EachSV 502 may further include a clock that is synchronized with otherclocks of SVs and with ground clock(s). If an SV 502 detects that thereis a drift from the time maintained on the ground, the SV 502 maycorrect it. The GNSS device 506 may also include a clock, but the clockfor the GNSS device 506 may be less stable and precise compared to theclocks for the SVs 502.

As the speed of radio waves may be constant and independent of thesatellite speed, a time delay between a time the SV 502 transmits a GNSSsignal 504 and a time the GNSS device 506 receives the GNSS signal 504may be proportional to the distance from the SV 502 to the GNSS device506. In some examples, a minimum of four SVs may be used by the GNSSdevice 506 to compute/calculate one or more unknown quantitiesassociated with positioning (e.g., three position coordinates and clockdeviation from satellite time, etc.).

Each SV 502 may broadcast the GNSS signal 504 (e.g., a carrier wave withmodulation) continuously that may include a pseudorandom code (e.g., asequence of ones and zeros) which may be known to the GNSS device 506,and may also include a message that includes a time of transmission andthe SV position at that time. In other words, each GNSS signal 504 maycarry two types of information: time and carrier wave (e.g., a modulatedwaveform with an input signal to be electromagnetically transmitted).Based on the GNSS signals 504 received from the SVs 502, the GNSS device506 may measure the time of arrivals (TOAs) of the GNSS signals 504 andcalculate the time of flights (TOFs) for the GNSS signals 504. Then,based on the TOFs, the GNSS device 506 may compute its three-dimensionalposition and clock deviation, and the GNSS device 506 may determine itsposition on the Earth. For example, the GNSS device 506's location maybe converted to a latitude, a longitude, and a height relative to anellipsoidal Earth model. These coordinates may be displayed, such as ona moving map display, or recorded or used by some other system, such asa vehicle guidance system.

While the distance between a GNSS device and an SV may be calculatedbased on the time it takes for a GNSS signal to reach the GNSS device,the SV's signal sequence may be delayed in relation to the GNSS device'ssequence. Thus, in some examples, a delay may be applied to the GNSSdevice's sequence, such that the two sequences are aligned. For example,to calculate the delay, a GNSS device may align a pseudorandom binarysequence contained in the SV's signal to an internally generatedpseudorandom binary sequence. As the SV's GNSS signal takes time toreach the GNSS device, the SV's sequence may be delayed in relation tothe GNSS device's sequence. By increasingly delaying the GNSS device'ssequence, the two sequences may eventually be aligned. The accuracy ofGNSS positioning may depend on various factors, such as SV geometry,GNSS signal blockage, atmospheric conditions, and/or GNSS receiverdesign features/quality, etc. For example, GNSS receivers used bysmartphones or smart watches may have an accuracy lower than GNSSreceivers used by vehicles and surveying equipment.

In some scenarios, non-line-of-sight (NLOS) GNSS measurements (e.g.,measurement of GNSS signal by a GNSS device) may degrade outdoorposition fix accuracy, such as within urban areas. For example, if thereare obstacles, such as physical structures (e.g., buildings, tunnels)and terrains (e.g., mountains), between SVs and a GNSS device, the GNSSsignals received by the GNSS device may be weakened and/or include anoffset/delay.

FIG. 6 is a diagram 600 illustrating an example of NLOS GNSSmeasurements in an urban area in accordance with various aspects of thepresent disclosure. A GNSS device 606 may receive GNSS signals from anSV 602 based on a line-of-sight (LOS) condition (which may be referredto as LOS signals). However, due to blockage and the environmentalconditions, the GNSS device 606 may receive GNSS signals from other SVs604 based on NLOS conditions (which may be referred to as NLOS signals),where the GNSS signals from the SVs 604 may penetrate through and/orbounce off from one or more objects (e.g., buildings, trees, etc.)before reaching the GNSS device 606. As NLOS signals may typically takea longer time to reach a GNSS device compared to LOS signals, the NLOSsignals may include excess delays. Thus, the positioning accuracy andspeed of the GNSS device may be reduced when a number of GNSSmeasurements are based on NLOS conditions. This may in turn reduce theefficiency and accuracy of certain location based service (LBS)applications, such as navigation applications and ride-shareapplication. In addition, the reduction in accuracy may be moresignificant during a GNSS device warm-start with large (e.g.,approximate one kilometer) initial time and position uncertainty. Insome examples, the continuous fix accuracy may also be degraded.

Aspects presented herein may improve the performance and accuracy ofGNSS-based positioning. Aspects presented herein provide a machinelearning (ML) model that utilises SV geometry and raw GNSS observablesto classify GNSS pseudorange (PR) measurements in terms of the relativeNLOS error (e.g., excess delays). Based on the ML classification, a GNSSdevice (e.g., a UE or a location server) may determine a suitableweighting for the PR measurements in GNSS position estimators (e.g.,weight least square (WLS), Kalman filter (KF), etc.).

For purposes of the present disclosure, an “inference” or an “MLinference” may refer to a process of running data points into an MLmodel (e.g., via an inference host) to calculate an output such as asingle numerical score, e.g., to use a trained ML algorithm to make aprediction. An “inference host” or an “ML inference host” may refer to anetwork function which hosts the ML model during an inference mode(described in details in connection with FIG. 7 ). Alternately, inanother aspect of the present disclosure, a GNSS device (e.g., a UE) maybe configured to employ computation resources and ML models to processPR measurements from different SVs and feed training data to an MLtraining host, where the ML training host may be collocated/associatedwith a server (e.g., a positioning server) for offline and/or onlinetraining of the ML models for classifying (e.g., predicting/estimating)GNSS PR measurements. Similarly, for purposes of the present disclosure,a “training” or an “ML training” may refer to a process of running datapoints to train or teach an ML model (e.g., via a training host). A“training host” or an “ML training host” may refer to a network functionwhich hosts the ML model during a training mode (described in details inconnection with FIG. 7 ). In addition, the term “pseudo-range” (orpseudorange) may refer to a pseudo distance between an SV (e.g., asatellite) and a navigation satellite receiver, such as a GNSS receiver.For example, for a navigation satellite receiver to determine itsposition, the satellite navigation receiver may determine the ranges tomultiple satellites as well as their positions at time of transmitting.Knowing the satellites' orbital parameters, these positions may becalculated for any point in time. The pseudoranges of each satellite maybe obtained by multiplying the speed of light by the time the signal hastaken from the satellite to the receiver.

FIG. 7 is a diagram 700 illustrating an example architecture of afunctional framework associated with an ML model (which may also bereferred to as an ML classifier) in accordance with various aspects ofthe present disclosure. In some scenarios, the functional frame work foran ML model may be enabled by further enhancement of data collectionthrough uses cases and/or examples. In one example, as shown by thediagram 700, a functional framework for the ML model may includemultiple logical entities, such as a model training host 702, a modelinference host 704, data sources 706, and/or an actor 708, etc. In someexamples, multiple logical entities may be co-located on the same device(e.g., a UE, a positioning device, etc.) or a network node (e.g., a basestation, a component of the base station, a server, etc.). In otherexamples, different logical entities may be located at different devicesor network nodes.

The model inference host 704 may be configured to run an ML model basedon inference data provided by the data sources 706, and the modelinference host 704 may produce an output (e.g., a prediction) with theinference data input to the actor 708. The actor 708 may be a device oran entity. For example, the actor 708 may be a GNSS device or a locationserver associated with the GNSS device, etc. In addition, the actor 708may also depend on the type of tasks performed by the model inferencehost 704, type of inference data provided to the model inference host704, and/or type of output produced by the model inference host 704,etc.

After the actor 708 receives an output from the model inference host704, the actor 708 may determine whether or how to act based on theoutput. For example, if the actor 708 is a location server and theoutput from the model inference host 704 is associated with PRmeasurement classification, the actor 708 may determine how to classifyone or more PR measurements performed based on the output. Then, theactor 708 may indicate the classification to at least one subject ofaction 710. In some examples, the actor 708 and the at least one subjectof action 710 may be the same entity (e.g., the GNSS device). Forexample, if the actor 708 (e.g., a location server) providesclassification and/or weighting for certain PR measurements performed bya subject of action 710 (e.g., a GNSS device), the actor 708 maytransmit a PR classification/weighting configuration to the subject ofaction 710. In response, the subject of action 710 may apply theclassification/weighting configuration to its PR measurements.

The data sources 706 may also be configured for collecting data that isused as training data for training the ML model or as inference data forfeeding an ML model inference operation. For example, the data sources706 may collect data from one or more GNSS devices or location servers,which may include the subject of action 710, and provide the collecteddata to the model training host 702 for ML model training. For example,after a subject of action 710 (e.g., a GNSS device) receives a PRmeasurement classification/weighting configuration from the actor 708(e.g., a location server), the subject of action 710 may provideperformance feedback associated with the PR measurementclassification/weighting configuration to the data sources 706, wherethe performance feedback may be used by the model training host 702 formonitoring or evaluating the ML model performance, e.g., whether theoutput (e.g., prediction) provided by the actor 708 is accurate. In someexamples, if the output provided by the actor 708 is inaccurate (or theaccuracy is below an accuracy threshold), the model training host 702may determine to modify or retrain the ML model used by the modelinference host, such as via an ML model deployment/update.

In one aspect of the present disclosure, an ML model is provided toclassify GNSS PR measurements in terms of relative NLOS error based onSV geometry. As such, the classification provided by the ML model mayenable a positioning device (e.g., a GNSS device) or a positioningserver to determine a more suitable weighting of each PR measurement inGNSS position estimators, which may include position estimation based onweighted least squares (WLS) (e.g., a generalization of ordinary leastsquares and linear regression in which knowledge of the variance ofobservations is incorporated into the regression) and/or based on Kalmanfilter (KF) (e.g., an algorithm that uses a series of measurementsobserved over time, including statistical noise and other inaccuracies,and produces estimates of unknown variables that tend to be moreaccurate than those based on a single measurement alone, by estimating ajoint probability distribution over the variables for each timeframe).In one example, the training of the ML model may be based on PR errorestimates (e.g., the data sources 706) provided by a higher performancepositioning device, such as an atomic clock accurate hardware. An atomicclock may refer to a clock that measures time by monitoring thefrequency of radiation of atoms based on atoms having different energylevels.

FIG. 8A is a diagram 800A illustrating an example ML training for an MLmodel that is capable of classifying GNSS PR measurements (e.g., interms of relative NLOS error) in accordance with various aspects of thepresent disclosure. In one example, the training of an ML model may beperformed offline at an ML workstation/server. For example, a modeltraining host (e.g., the model training host 702) may receive featuresfrom a data source (e.g., the data sources 706), such as a higherperformance positioning device with atomic clock accurate hardware or anend user device (e.g., the actor 708 and/or the subject of action 710),for training an ML model for classifying GNSS PR measurements in termsof relative NLOS error (may be referred to as an “ML classifier” or a“weight based ML classifier” hereafter) as signals received by a GNSSdevice via an NLOS path may include more errors and/or higher delayscompared to signals received via an LOS path. In one example, thefeatures used for training the weight based ML classifier may includeraw GNSS SV observables (e.g., raw GNSS measurements), such ascarrier-to-noise ratio (C/No), elevation/azimuth angle, auto-correlationfunction (ACF), code-carrier phase consistency, measurement status anderror flags from hardware and measurement engine, consistency checkbetween different frequency bands measurements (e.g., between L1 bandand L5 band measurements) from the same SV, weighted least squaresa-posteriori residuals, and/or signal (energy) integration informationfor each SV measurement (e.g., duration, mode, etc.), etc. Then, basedon the features collected from a plurality of SVs, the model traininghost may be configured to derive a relationship between the plurality ofSVs for the weight based ML classifier. For example, the relationshipmay be based on the SV geometry, such as geometric orientation of a setof SVs respect to a GNSS device, where the geometric orientation may bea function of at least an azimuth angle (or an elevation angle) and azenith angle between the GNSS device and a corresponding SV of the setof SVs.

FIG. 9 is a diagram 900 illustrating an example SV geometry inaccordance with various aspects of the present disclosure. In oneexample, the training of the weight based ML classifier may be based onthe geometry of SVs, such as based on the spherical distance between SVswith respect to a ground point (e.g., observed/measured by an GNSSdevice). For example, the graph shown at 902 is an example skyplot of aplurality of SVs observed by a GNSS device at a ground point (e.g., SVlocations relative to the GNSS device). The skyplot may provide anillustration of SV trajectories over a given ground site and/or thespherical distance between SVs. As shown at 904, the spherical distancebetween two SVs (e.g., SV(j) and SV(i)) may refer to a distance along agreat circle of the two SVs.

In one aspect of the present disclosure, if the spherical distancebetween two SVs are below a distance threshold (e.g., the distancebetween two SVs is low), there may be a correlation (in terms ofmeasurements) between the two SVs from at least the perspective of aGNSS device. For example, as shown at 902, as SV3 and SV4 are closer toeach other compared to other SVs (e.g., SVs 1, 2, 5, 7, and 9, etc.),signals measured from SV3 and SV4 are likely to share some commonconditions (e.g., LOS condition, NLOS condition, etc.) and/or errors(e.g., ionospheric/tropospheric errors). As such, measurements (e.g.,the features/raw measurements) of SV3 may be used to assist measurementsof SV4 and vice versa. For example, if a GNSS device measures signalsfrom SV3 and derives a set of errors associated with SV3, the GNSSdevice may be configured to assume the same set of errors are alsoapplicable to signals from SV4. In another example, if a GNSS devicereceives signals from SV4 via an NLOS path, the GNSS device may alsoassume that the signals received from SV3 are based on an NLOS path. Assuch, referring back to FIG. 8A, based at least in part on the geometrybetween SVs and features (e.g., raw GNSS SV observables) obtained fromthe SVs, the weight based ML classifier may be trained to provide arelative weight for a PR measurement from an SV based on measurementsfrom one or more other SVs, and the weight based ML classifier may dothe same for all PR measurements from multiple SVs to provide relativeweights for these PR measurements (which also may be referred to asrelative PR weight). For example, as SVs that are closer to each othermay have similar errors, the relative PR weight may be configured to afunction associated with the errors (which may be referred to as PRerrors). As such, a weight based ML classifier may be trained toclassify the likelihood of LOS and/or NLOS conditions, where PRmeasurements that may be based on NLOS condition may be provided with alower weight compared to PR measurements that are based on LOScondition. In other words, the relative PR weight for one SV in a set ofSVs may be based on a spherical distance between the one SV and each SVof at least a subset of the set of SVs, and the spherical distancebetween the one SV and an other SV may be based on the geometricorientation of the one SV compared to the geometric orientation of theother SV. In addition, the relative PR weight for one SV of the set ofSVs may be further based on one or more of a carrier-to-noise ratio, anauto-correlation function, a code-carrier phase consistency, measurementstatus or error flags, consistency between different band measurementsof the one SV, weighted least squares a-posteriori residuals, and/orsignal integration information, etc.

In one example, the weight based ML classifier may be trained based on agraph convolutional network (GCN). GCN is a type of convolutional neuralnetwork (CNN) that is capable of working directly on graphs and takeadvantage of their structural information. GCN may be employed toclassify nodes in a graph, where labels may be available for a smallsubset of nodes. For example, as shown by the diagram at 902 of FIG. 9 ,each SV in a skyplot may be represented by a node in GCN, and two nodes(e.g., two SVs) may be connected to each other if their sphericaldistance is below a distance threshold. For example, SV 3 may beconnected to SVs 4 and 7, SV 7 may be connected to SVs 3, 4, 5, and 9,and SV 1 may be connected to SV 5, etc. As such, it may be assumed thateach node may have some relationship with the other nodes in the graph,represented in the edge connection and edge weight (spherical distancebetween SVs). Each connected node may provide additional information toeach other by sharing their respective node features, through anneighbor node aggregate method (which may be referred to as‘message-passing’). There may also be other variants and alternatives tomessage-passing.

FIG. 10 is a diagram 1000 illustrating an example ML training based on aGCN in accordance with various aspects of the present disclosure. Atarget node may use aggregated neighborhood node features to make aprediction via the neural network, which may be a task like nodeclassification, or structure/context determination. For example, duringan ML training, the target node (e.g., a target SV) may first sampleneighboring nodes (e.g., other SVs) for candidate nodes (e.g., SVs withspherical distance below a threshold). Then, the target nodes mayaggregate feature information (e.g., raw GNSS SV observables) from thesecandidate nodes, and the target node may predict a graph context andlabel based on the aggregated feature information. In other words, agraph may be constructed to represent SV measurements based on theirgeometry (e.g., skyplot), then a graph neural network (GNN) (e.g. GCN)and GNN-like ML methods (e.g., non-seasonal variation (NSV), multilayerperceptron (MLP)) to effectively use neighbor SV measurements to aid inmain SV classification.

FIG. 8B is a diagram 800B illustrating an example ML inferencing for anML model that is capable of classifying GNSS PR measurements inaccordance with various aspects of the present disclosure. After theweight based ML classifier discussed in connection with FIG. 8A istrained, a GNSS device or a location server may use the weight based MLclassifier for classifying PR measurements from different SVs. Forexample, a GNSS device may measure PR for a plurality SVs, and theweight based ML classifier may provide a relative PR weight for each PRmeasured. For example, referring to the diagram at 902 of FIG. 9 , aGNSS device may measure PRs for SVs 1, 2, 3, 4, 5, 7, and 9, and theweight based ML classifier may provide a PR weight for each of the PRmeasurements from each SV based on measurements from other SVs. Then,the GNSS device may perform position estimation based on the weighted PRmeasurements, which may provide a higher positioning accuracy comparedto non-weighted PR measurements. As such, by using accurate PR errorlabel (e.g., obtained from a third party truth position device andatomic clock reference) to construct the label data for ML training, anML model may construct a classification label data based on the PR errorlabel, such as based on combination of absolute PR error and relativedifference within a given fix epoch (a graph). An epoch may refer to amoment in time used as a reference point for some time-varyingastronomical quantity.

In another aspect of the present disclosure, to enable deployment ofaspects presented herein on GNSS devices with lower computationalresources, the weight based ML classifier may be trained and provideinference based on non-seasonal variation (NSV) and/or multilayerperceptron (MLP), which may reduce the complexity of the ML trainingand/or ML inferencing with performance/accuracy of the positioningslightly reduced. MLP may refer to a neural network where the mappingbetween inputs and output may be non-linear. For example, an MLP may bea class of feedforward artificial neural network (ANN), which may referto networks composed of multiple layers of perceptron (with thresholdactivation). An MLP may include at least three layers of nodes: an inputlayer, one or more hidden layers, and an output layer. Except for theinput nodes, each node may be a neuron that uses a nonlinear activationfunction. The inputs may be combined with initial weights in a weightedsum and subjection to the activation function. Then, each layer(starting from the input layer) may feed the next layer with the resultof their computation and/or internal representation of the data, whichmay go through all the way to the output layer. In some examples, theMLP may utilize a supervised learning technique called backpropagationfor training. Backpropagation may refer to a learning mechanism thatallows the MLP to iteratively adjust the weights in the network, withthe goal of minimizing the cost function. An output may be generatedfrom multiple inputs based on MLP.

FIG. 11 is a diagram 1100 illustrating an example architecture ofapproximating GCN classifier based on MLP in accordance with variousaspects of the present disclosure. For a weight based ML classifierbased on MLP, features from multiple SVs may be provided to the frontend of the MLP (e.g., the input layer of the MLP). Then, the featuresmay be aggregated based on their weights through one or more hiddenlayers of the MLP to generate a relative PR weight for each SV (withrespect to other SVs). For example, to determine the relative PR weightfor SV(i), features (e.g., measurements) from SV(i) and other SVs (e.g.,SV(j), SV(k), SV(l), . . . , etc.) may be provided to an MLP. Then,based on the ML training, a relative PR weight for SV(i) may beobtained. While the ML classifier based on the MLP may have a lowerperformance and accuracy compared to the ML classifier based on GCN, theperformance trade-off may be acceptable in some scenarios as there maybe a significant reduction in memory and CPU usage.

FIG. 12 is a diagram 1200 illustrating an example of horizontal errorcumulative distribution functions (CDFs) for ML classifiers based on MLPand GCN in accordance with various aspects of the present disclosure.The accuracy of a GNSS device may be measured based on a horizontalcomponent (e.g., easting and northing) accuracy, which may be expressedby a circular error probable (CEP). A CEP may indicate that there is a50% chance that the true horizontal position is located inside a circleof radius equal to the value of CEP. As shown by the diagram 1200, bothan ML classifier based on GCN and an ML classifier based on MLP mayprovide better positioning performance compared to the baselineperformance (e.g., without ML classifier/classification). While the MLclassifier based on GCN may provide a better performance in terms ofhorizontal error compared to the ML classifier based on MLP (e.g., MLclassifier based on GCN is closer to truth-based performance compared tothe ML classifier based on MLP), the ML classifier based on MLP consumesrelatively smaller memory (e.g., approximately 33 KB) compared to the MLclassifier based on GCN (e.g., approximately 11.2 MB). As such, in somescenarios, the ML classifier based on MLP may be a more suitable choicefor GNSS devices with less memory availabilities or for GNSS deviceswith reduced capabilities.

FIG. 13 is a diagram 1300 illustrating an example of receiver operationcharacteristics for ML classifiers based on MLP and GCN in accordancewith various aspects of the present disclosure. The diagram 1300illustrates the performance of ML classifiers based on MLP and GCN interms of receiver operation characteristics based on the area under thecurve (AUC). AUC is a measure of the ability of a classifier todistinguish between classes. In general, the higher the AUC, the betterthe performance of the model at distinguishing between the positive andnegative classes. As illustrated by the diagram 1300, ML classifierbased on GCN may provide a better performance in terms of receiveroperation characteristics compared to the ML classifier based on MLP.Thus, in some scenarios, the ML classifier based on GCN may be a moresuitable choice for GNSS devices with sufficient memory availabilitiesor for GNSS devices that specify higher positioning accuracy and/orspeed.

In another aspect of the present disclosure, a position-grid based MLclassifier is provided to a GNSS device to improve the accuracy ofidentifying a warm-start position of the GNSS device. The warm start ofa GNSS device may refer to a scenario where the GNSS device remembersits last calculated position or is able to estimate its initial position(e.g., based on location of the serving base station or an accesspoint), almanac used, and UTC Time, but not which SVs were in view. TheGNSS device may then perform a reset and attempt to obtain the SV GNSSsignals and calculate a new position. The GNSS device may have a generalidea of which SVs to look for based on the last known position and thealmanac data may help the GNSS device to identify which satellites arevisible in the sky. This process may take some time.

In one aspect, a GNSS device may be configured to estimate its initialposition and establish a position-grid with a fixed resolution (e.g.,with multiple grid points) that covers a region of position uncertaintybased on the estimated initial position. Then, the GNSS device maycompute per SV PR measurement residuals at each grid point of theposition-grid. A position-grid based ML classifier may be trained toprocess per SV GNSS observables and PR measurement residuals obtained bythe GNSS device to infer/predict the probability of whether a grid pointis near the actual position of the GNSS device. For example, theposition-grid based ML classifier may be trained based on PR errorestimates derived from atomic clock accurate and/or the known distancebetween the GNSS device position and the grid point position, and theposition-grid based ML classifier may estimate the GNSS device locationusing the distribution of grid point probabilities across theposition-grid.

FIG. 14 is a diagram 1400 illustrating an example of known and unknownparameters for a GNSS device during a warm start in accordance withvarious aspects of the present disclosure. During a warm start of a GNSSdevice 1402, the GNSS device 1402 may be able to identify: (1) GNSS PRMeasurement: raw range measurement obtained from GNSS measurement engine(e.g., based on local GNSS arrival time minus GNSS transmit time); (2)SV Position: position of an SV 1404, derived from ephemeris or XTRA; and(3) Initial Estimated GNSS Device Position: initial position of the GNSSdevice 1402 used for the fixed location calculation. On the other hand,the GNSS device 1402 may not be able to identify one or more of thefollowings: (a) Actual GNSS Device Position: actual position of the GNSSdevice 1402; (b) SV Range: distance from the position of the SV 1404 tothe position of the GNSS device 1402, which may be unique to eachindividual SV; (c) SV Range Error: Error in SV range as computed basedon the Initial Estimated GNSS Device Position and the SV Position, whichmay be unique to each individual SV (e.g., typical errors are −1 km to+1 km); (d) Measurement Error: error in the measurement due to noiseand/or excess delay (e.g., typical noise error=+/−10 m, and typicalexcess delay error=+20 m to +300 m); and/or (e) Clock Error: error inGNSS time determined from the local receiver clock, which may be commonto all PR measurements in the same epoch (e.g., typical clock error is−10 km to +10 km). As such, in order to determine the SV range, the GNSSdevice 1402 may be specified to solve or determine the three sources oferror (e.g., the clock error, the noise error, and the excess delayerror).

For example, to determine the SV range error, the GNSS device 1402 maybe configured to differentiate whether a measurement error includes boththe noise error and the excess delay error (referring as “case A”hereafter), or the measurement error includes the noise error withoutthe excess delay error (referring as “case B” hereafter). Case Ameasurements are more likely to associated be with NLOS measurements(e.g., GNSS signals are received based on NLOS condition or multipaths)as they include excess delay error, whereas case B measurements are morelikely to be associated with LOS measurements (e.g., GNSS signals arereceived based on LOS condition) as they do not include excess delayerror (thus providing a better distance estimation). In some examples,knowledge of the clock error may be skipped as the clock error may becommon to all PR measurements. In some scenarios, for a set of N PRmeasurements (e.g., typically N=15 to 50), the initial estimated GNSSdevice position (and therefore the SV range error) may constantly bechanged to make case A measurements appear like case B measurements andvice versa. As such, in one aspect of the present disclosure, an MLclassifier (which may be referred to as a “position-grid based MLclassifier” hereafter) may be trained to classify whether PRmeasurements are associated with case A or case B because typicallythere may be a subset of case A measurements among the N PR measurementsand the measurement error may be specified to be modeled accurately. Forexample, the position-grid based ML classifier may be trained toinitially differentiate case A from case B for the GNSS device 1402, andthen the GNSS device 1402 may be able to estimate a more accurate clockerror and receiver position. In other words, if the position-grid basedML classifier works properly, the measurement errors post-computed withthe estimated clock error and the actual receiver position may either benoise error or excess delay error plus noise error (e.g., no measurementerror may indicate early arrival).

FIG. 15 is a diagram 1500 illustrating an example of identifying aposition of a GNSS device (e.g., the GNSS device 1402) based on aposition-grid approach in accordance with various aspects of the presentdisclosure. In one example, during a warm start, the GNSS device 1402may be configured to define a two-dimensional (2D) rectangular positiongrid, centered approximately at the initial estimated GNSS deviceposition with grid points spaced at a defined distance (e.g., X mapart). The GNSS device 1402 may be specified to consider just the gridpoints that fall within a region of position uncertainty (e.g., acircular region) defined by an initial position uncertainty (e.g.,within a radius of Y meters from the initial estimated GNSS deviceposition).

Then, for each grid point (G) within the position-grid, the GNSS device1402 may compute the PR measurement residuals for the set of PRmeasurements available at the current epoch, where a PR measurementresidual may equal to a difference between a PR measurement minus apredicted PR (e.g., PR Measurement Residual=PR Measurement−PR Predicted,where PR Predicted=range of G to SV 1404 and certain correction factorsmay be excluded for simplicity). The PR measurement residuals measuredat a grid point may provide how PR measurement residuals would look atthat grid point if the GNSS device 1402 is at that grid point. Forexample, assuming there is a total of ten (10) PR measurements (e.g.,measured from ten SVs) that include five (5) case A measurements (e.g.,PR measurements including excess delay error and noise error) and five(5) case B measurements (e.g., PR measurements including just noiseerror), which may be differentiated by the GNSS device 1402 based on atrained position-grid based ML classifier. As shown at 1406, when a gridpoint (e.g., grid point G(j)) that is used for calculating the PRmeasurement residuals is further away from the actual GNSS deviceposition (e.g., nine grid spacing away), case A measurements and case Bmeasurements may tend to distribute randomly across the early points andlate points of the distribution. On the other hand, as shown at 1408,when a grid point (e.g., grid point G(i)) that is used for calculatingthe PR measurement residuals is closer to the actual GNSS deviceposition (e.g., one grid spacing away), case B measurements may bedistributed/clustered at the earliest extent of the distribution, whilecase A measurements may be uniformly distributed at later points of thedistribution. In other words, based on the distribution of case A andcase B measurements, the GNSS device 1402 may estimate its actualposition more quickly and accurately. In other words, the GNSS device1402 is more likely to locate approximate (e.g., close) to grid pointswith case A and case B measurements distributed as shown at 1408, andless likely to locate approximate to grid points with case A and case Bmeasurements distributed as shown at 1406.

When a measured grid point (e.g., G(i)) is closer to the truth positionof the GNSS device 1402, good PR measurement residuals (e.g.,measurements without excess delay) are likely to distribute/cluster onthe early side of the distribution because the nature of the GNSSreflections is a delay, which may be random (e.g., it is a function ofhow SV signals reflected on the buildings and other obstacles). Thus, ifa grid point is approximate (or close) to the truth position of the GNSSdevice 1402, there is a signature where the good measurements (e.g.,case B measurements) are distributed/clustered in an early portion of adistribution and the not good measurements (e.g., case A measurements)are distributed/clustered in a later portion of the distribution as theyare delayed. Such clustering associated with G(i) is due to theestimated PR, which is a function of G(i), being closer to the measuredPR (e.g., the truth position of the GNSS device 1402). However, if ameasured grid point is not approximate to the truth position of the GNSSdevice, the case A measurements and the case B measurements are likelyto be jumbled because errors between this measured grid point and thetruth location of the GNSS may also be random. As there are basicallytwo sources of error, one is due to the reflection (e.g., the NLOScondition) and the other one is the error for the GNSS device position,they both may be projected in the prediction of the distance to thesatellite. Thus, the two sources of errors may constructively ordestructively interfere with each other, thereby creating thedistribution/clustering pattern as shown at 1406. Such clusteringassociated with G(j) is due to the estimated PR, which is a function ofG(j), being further from the measured PR (e.g., the truth position ofthe GNSS device 1402).

Similarly, in another aspect of the present disclosure, an ML classifier(e.g., an additional ML classifier or the position-grid based MLclassifier) may be trained to identify whether a grid point isapproximate to the actual position of the GNSS device 1402, and therebyestimating the actual location of the GNSS device 1402. For example, aposition-grid based ML classifier may be trained to first identifywhether PR measurements from multiple SVs at an epoch are case Ameasurements or case B measurements, and then the position-grid based MLclassifier may determine the probability of whether a grid point isapproximate to the actual position of the GNSS device 1402 based on theclustering/distribution patterns of the case A and case B measurements.Based on identifying the likelihood of whether each of the grid pointswithin the region of position uncertainty is approximate to the GNSSdevice 1402, the GNSS device 1402 may create a 2D heat map that showsthe probabilities (e.g., in terms of percentage such as 50% or 75%) orthe likelihoods (e.g., in terms relative comparison such as low, mediumand high) of whether the grid points are close to the actual position ofthe GNSS device 1402.

FIG. 16 is a diagram 1600 illustrating an example position-grid based MLclassifier for inferencing whether a grid point is approximate to anactual position of a GNSS device in accordance with various aspects ofthe present disclosure. A position-grid based ML classifier may use allavailable information for training and inference. For example, theposition-grid based ML classifier may use measurement error for all PRmeasurements and/or actual GNSS device position as labels, and use GNSSmeasurement observables for each SV (e.g., C/No, code/carrierconsistency, carrier phase availability, weighted least squaresa-posteriori residuals, etc.), geometric relation between SVs (e.g.spherical distance as described in connection with FIG. 9 ), and/ordistribution of PR measurements residuals computed at a grid point asfeatures for ML training and inferencing. For example, the position-gridbased ML classifier may be trained based on a multiclass GCN forestimating weight probabilities for all SVs. Then, another per gridpoint ML classifier may further be trained to receive weightprobabilities for each SV and/or PR measurement residual for each SV asfeatures, and to generate a label for a grid point based on the gridspacing between the grid point and the actual GNSS device position(e.g., TRUE if grid point is <=1 grid spacing away from actual GNSSdevice position and FALSE if grid point is >1 grid spacing away fromactual GNSS device position, etc.). Thus, during the ML inference, theposition-grid based ML classifier may compute a probability that eachgrid point is within 1 grid spacing away from the actual GNSS deviceposition, and the position-grid based ML classifier may declare thelocation of the actual GNSS device position based on the 2D heat map ofgrid point probabilities.

In another aspect of the present disclosure, thelikelihoods/probabilities of whether the grid points are approximate tothe actual location of the GNSS receiver and/or the sets of PRs for eachgrid point (e.g., for case A measurements and case B measurements) mayfurther be provided as features for the weight based ML classifierdescribed in connection with FIGS. 8A, 8B, 9, 10, and 11 , such that theweight based ML classifier may determine the weights for PR measurementsfrom different SVs further based on the likelihoods/probabilities and/orsets of PRs for each grid point. Then, the GNSS device or a locationserver may determine or estimate the location of the GNSS device basedon the weighted PR measurements, which may further enhance the accuracyof the GNSS device positioning.

In another aspect of the present disclosure, a first ML classifier(i.e., a weight based ML classifier) may determine the position of aGNSS device based on weighted PR measurements (e.g., as described inconnection with FIGS. 8A, 8B, 9, 10, and 11 ) and a second ML classifier(i.e., a position-grid based ML classifier) may determine the positionof the GNSS device based on grid points (e.g., as described inconnection with FIGS. 14 to 16 ). Then, a third ML classifier mayreceive the determined positions of the GNSS device from both the firstML classifier and the second ML classifier, and the third ML classifiermay weight the positions from the first and the second ML classifiersand provide an estimated GNSS device position based on the weightedpositions.

FIG. 17 is a flowchart 1700 of a method of position estimation. Themethod may be performed by a positioning device or a positioning entity(e.g., the UE 104; the base station 102; the GNSS device 506, 606, 1402;the apparatus 1904; the network entity 1902). The method may enable thepositioning device (e.g., a UE) or the positioning entity (e.g., alocation server) to estimate a warm start position of the positioningdevice based on a position-grid approach to improve the accuracy andspeed of identifying the position of the positioning device.

At 1702, the positioning device or the positioning entity may measure,for each SV of a set of SVs, the PR between the positioning device andthe SV, such as described in connection with FIG. 15 . For example, aGNSS device 1402 (e.g., the positioning device) may measure, for each SVof a set of SVs, the PR between the GNSS device and the SV. Themeasurement of the PR may be performed by, e.g., the positioning MLcomponent 198 of the apparatus 1904 and/or the positioning ML component199 of the network entity 1902 in FIG. 19 .

At 1704, the positioning device or the positioning entity may determine,for each grid point within a range of an initial position of apositioning device, a set of PR residuals based on PRs for each SV of aset of SVs, such as described in connection with FIG. 15 . For example,the GNSS device 1402 may determine, for each grid point within a rangeof an initial position of the GNSS device 1402, a set of PR residualsbased on PRs for each SV of a set of SV. The determination of the set ofPR residuals may be performed by, e.g., the SPS module 1916/thepositioning ML component 198 of the apparatus 1904, and/or thepositioning ML component 199 of the network entity 1902 in FIG. 19 .

In one example, the PRs may include a measured PR and a predicted PR,and each PR residual in the set of PR residuals may be a measure of adifference between the measured PR and the predicted PR.

At 1706, the positioning device or the positioning entity may predict,for each grid point and for each SV of the set of SVs, the PR betweenthe positioning device and the SV at the grid point, such as describedin connection with FIG. 15 . For example, the GNSS device 1402 maypredict, for each grid point and for each SV of the set of SVs, the PRbetween the GNSS device 1402 and the SV at the grid point. Thedetermination of the relative PR weight may be performed by, the SPSmodule 1916/the positioning ML component 198 of the apparatus 1904and/or the positioning ML component 199 of the network entity 1902 inFIG. 19 . In such an example, each PR residual in the set of PRresiduals may be determined based on a difference between the measuredPR and the predicted PR.

At 1708, the positioning device or the positioning entity may determinean estimated position of the positioning device based on the sets ofdetermined PR residuals, such as described in connection with FIG. 15 .For example, the GNSS device 1402 may estimate a position of thepositioning device based on the distribution of PR residuals. Theestimation of the position of the positioning device may be performedby, e.g., the SP S module 1916/the positioning ML component 198 of theapparatus 1904, and/or the positioning ML component 199 of the networkentity 1902 in FIG. 19 .

At 1710, the positioning device or the positioning entity may determine,based on an ML classifier, a likelihood of whether the positioningdevice is approximate to the grid point based on the corresponding setof determined PR residuals, where the estimated position of thepositioning device may be determined based on the determinedlikelihoods, such as described in connection with FIG. 15 . Thedetermination of the likelihood of whether the positioning device isapproximate to the grid point may be performed by, e.g., the SPS module1916/the positioning ML component 198 of the apparatus 1904, and/or thepositioning ML component 199 of the network entity 1902 in FIG. 19 .

In one example, the likelihood of whether the positioning device isapproximate the grid point may be classified by the ML classifier basedon a distribution of PR residuals in each set of PR residuals.

In another example, the positioning device or the positioning entity maydetermine for each SV of the set of SVs at least a geometric orientationwith respect to the UE, and determining, based on an ML classifier andthe determined geometric orientation with respect to the UE for each SVof at least a subset of the set of SVs, a relative PR weight for each SVof the set of SVs, where the estimated position of the UE is furtherdetermined based on the relative PR weight for each SV of the set ofSVs. In such an example, the geometric orientation may be a function ofat least an azimuth angle and a zenith angle between the UE and acorresponding SV of the set of SVs. In such an example, the relative PRweight for one SV of the set of SVs may be based on a spherical distancebetween the one SV and each SV of the at least the subset of the set ofSVs, the spherical distance between the one SV and an other SV beingbased on the geometric orientation of the one SV compared to thegeometric orientation of the other SV. In such an example, the relativePR weight for one SV of the set of SVs may be further based on one ormore of a carrier-to-noise ratio, an auto-correlation function, acode-carrier phase consistency, measurement status or error flags,consistency between different band measurements of the one SV, weightedleast squares a-posteriori residuals, or signal integration information.In such an example, the relative PR weight may be based on a predictedrelative PR error by the ML classifier.

FIG. 18 is a flowchart 1800 of a method of position estimation. Themethod may be performed by a positioning device or a positioning entity(e.g., the UE 104; the base station 102; the GNSS device 506, 606, 1402;the apparatus 1904; the network entity 1902). The method may enable thepositioning device (e.g., a UE) or the positioning entity (e.g., alocation server) to estimate a warm start position of the positioningdevice based on a position-grid approach to improve the accuracy andspeed of identifying the position of the positioning device.

At 1804, the positioning device or the positioning entity may determine,for each grid point within a range of an initial position of apositioning device, a set of PR residuals based on PRs for each SV of aset of SVs, such as described in connection with FIG. 15 . For example,the GNSS device 1402 may determine, for each grid point within a rangeof an initial position of the GNSS device 1402, a set of PR residualsbased on PRs for each SV of a set of SV. The determination of the set ofPR residuals may be performed by, e.g., the SPS module 1916/thepositioning ML component 198 of the apparatus 1904, and/or thepositioning ML component 199 of the network entity 1902 in FIG. 19 .

In one example, the positioning device or the positioning entity maymeasure, for each SV of a set of SVs, the PR between the positioningdevice and the SV, such as described in connection with FIG. 15 . Forexample, a GNSS device 1402 (e.g., the positioning device) may measure,for each SV of a set of SVs, the PR between the GNSS device and the SV.The measurement of the PR may be performed by, e.g., the positioning MLcomponent 198 of the apparatus 1904 and/or the positioning ML component199 of the network entity 1902 in FIG. 19 .

In another example, the positioning device or the positioning entity maypredict, for each grid point and for each SV of the set of SVs, the PRbetween the positioning device and the SV at the grid point, such asdescribed in connection with FIG. 15 . For example, the GNSS device 1402may predict, for each grid point and for each SV of the set of SVs, thePR between the GNSS device 1402 and the SV at the grid point. Thedetermination of the relative PR weight may be performed by, the SPSmodule 1916/the positioning ML component 198 of the apparatus 1904and/or the positioning ML component 199 of the network entity 1902 inFIG. 19 . In such an example, each PR residual in the set of PRresiduals may be determined based on a difference between the measuredPR and the predicted PR.

In another example, the PRs may include a measured PR and a predictedPR, and each PR residual in the set of PR residuals may be a measure ofa difference between the measured PR and the predicted PR.

At 1808, the positioning device or the positioning entity may determinean estimated position of the positioning device based on the sets ofdetermined PR residuals, such as described in connection with FIG. 15 .For example, the GNSS device 1402 may estimate a position of thepositioning device based on the distribution of PR residuals. Theestimation of the position of the positioning device may be performedby, e.g., the SP S module 1916/the positioning ML component 198 of theapparatus 1904, and/or the positioning ML component 199 of the networkentity 1902 in FIG. 19 .

In one example, the positioning device or the positioning entity maydetermine, based on an ML classifier, a likelihood of whether thepositioning device is approximate to the grid point based on thecorresponding set of determined PR residuals, where the estimatedposition of the positioning device may be determined based on thedetermined likelihoods, such as described in connection with FIG. 15 .The determination of the likelihood of whether the positioning device isapproximate to the grid point may be performed by, e.g., the SPS module1916/the positioning ML component 198 of the apparatus 1904, and/or thepositioning ML component 199 of the network entity 1902 in FIG. 19 .

In another example, the likelihood of whether the positioning device isapproximate the grid point may be classified by the ML classifier basedon a distribution of PR residuals in each set of PR residuals.

In another example, the positioning device or the positioning entity maydetermine for each SV of the set of SVs at least a geometric orientationwith respect to the UE, and determining, based on an ML classifier andthe determined geometric orientation with respect to the UE for each SVof at least a subset of the set of SVs, a relative PR weight for each SVof the set of SVs, where the estimated position of the UE is furtherdetermined based on the relative PR weight for each SV of the set ofSVs. In such an example, the geometric orientation may be a function ofat least an azimuth angle and a zenith angle between the UE and acorresponding SV of the set of SVs. In such an example, the relative PRweight for one SV of the set of SVs may be based on a spherical distancebetween the one SV and each SV of the at least the subset of the set ofSVs, the spherical distance between the one SV and an other SV beingbased on the geometric orientation of the one SV compared to thegeometric orientation of the other SV. In such an example, the relativePR weight for one SV of the set of SVs may be further based on one ormore of a carrier-to-noise ratio, an auto-correlation function, acode-carrier phase consistency, measurement status or error flags,consistency between different band measurements of the one SV, weightedleast squares a-posteriori residuals, or signal integration information.In such an example, the relative PR weight may be based on a predictedrelative PR error by the ML classifier.

FIG. 19 is a diagram 1900 illustrating an example of a hardwareimplementation for an apparatus 1904. The apparatus 1904 may be a UE, acomponent of a UE, or may implement UE functionality. In some aspects,the apparatus 1904 may include a cellular baseband processor 1924 (alsoreferred to as a modem) coupled to one or more transceivers 1922 (e.g.,cellular RF transceiver). The cellular baseband processor 1924 mayinclude on-chip memory 1924′. In some aspects, the apparatus 1904 mayfurther include one or more subscriber identity modules (SIM) cards 1920and an application processor 1906 coupled to a secure digital (SD) card1908 and a screen 1910. The application processor 1906 may includeon-chip memory 1906′. In some aspects, the apparatus 1904 may furtherinclude a Bluetooth module 1912, a WLAN module 1914, an SPS module 1916(e.g., GNSS module), one or more sensor modules 1918 (e.g., barometricpressure sensor/altimeter; motion sensor such as inertial managementunit (IMU), gyroscope, and/or accelerometer(s); light detection andranging (LIDAR), radio assisted detection and ranging (RADAR), soundnavigation and ranging (SONAR), magnetometer, audio and/or othertechnologies used for positioning), additional memory modules 1926, apower supply 1930, and/or a camera 1932. The Bluetooth module 1912, theWLAN module 1914, and the SP S module 1916 may include an on-chiptransceiver (TRX) (or in some cases, just a receiver (RX)). TheBluetooth module 1912, the WLAN module 1914, and the SP S module 1916may include their own dedicated antennas and/or utilize the antennas1980 for communication. The cellular baseband processor 1924communicates through the transceiver(s) 1922 via one or more antennas1980 with the UE 104 and/or with an RU associated with a network entity1902. The cellular baseband processor 1924 and the application processor1906 may each include a computer-readable medium/memory 1924′, 1906′,respectively. The additional memory modules 1926 may also be considereda computer-readable medium/memory. Each computer-readable medium/memory1924′, 1906′, additional memory modules 1926 may be non-transitory. Thecellular baseband processor 1924 and the application processor 1906 areeach responsible for general processing, including the execution ofsoftware stored on the computer-readable medium/memory. The software,when executed by the cellular baseband processor 1924/applicationprocessor 1906, causes the cellular baseband processor 1924/applicationprocessor 1906 to perform the various functions described supra. Thecomputer-readable medium/memory may also be used for storing data thatis manipulated by the cellular baseband processor 1924/applicationprocessor 1906 when executing software. The cellular baseband processor1924/application processor 1906 may be a component of the UE 350 and mayinclude the memory 360 and/or at least one of the TX processor 368, theRX processor 356, and the controller/processor 359. In oneconfiguration, the apparatus 1904 may be a processor chip (modem and/orapplication) and include just the cellular baseband processor 1924and/or the application processor 1906, and in another configuration, theapparatus 1904 may be the entire UE (e.g., see 350 of FIG. 3 ) andinclude the additional modules of the apparatus 1904.

As discussed supra, the positioning ML component 198/199 is configuredto determine, for each grid point within a range of an initial positionof a UE, a set of PR residuals based on PRs for each SV of a set of SVs;and determine an estimated position of the UE based on the sets ofdetermined PR residuals. The positioning ML component 198 may be withinthe cellular baseband processor 1924, the application processor 1906, orboth the cellular baseband processor 1924 and the application processor1906. The positioning ML component 198/199 may be one or more hardwarecomponents specifically configured to carry out the statedprocesses/algorithm, implemented by one or more processors configured toperform the stated processes/algorithm, stored within acomputer-readable medium for implementation by one or more processors,or some combination thereof. As shown, the apparatus 1904 may include avariety of components configured for various functions. In oneconfiguration, the apparatus 1904, and in particular the cellularbaseband processor 1924 and/or the application processor 1906, includesmeans for determining, for each grid point within a range of an initialposition of a UE, a set of PR residuals based on PRs for each SV of aset of SVs; means for determining an estimated position of the UE basedon the sets of determined PR residuals; means for measuring, for each SVof the set of SVs, the PR between the UE and the SV; means forpredicting, for each grid point and for each SV of the set of SVs, thePR between the UE and the SV at the grid point; means for determining,based on a machine learning (ML) classifier, a likelihood of whether theUE is approximate to the grid point based on the corresponding set ofdetermined PR residuals, where the estimated position of the UE isdetermined based on the determined likelihoods; means for determiningfor each SV of the set of SVs at least a geometric orientation withrespect to the UE; and means for determining, based on an ML classifierand the determined geometric orientation with respect to the UE for eachSV of at least a subset of the set of SVs, a relative PR weight for eachSV of the set of SVs. In some examples, the means may be the positioningML component 198 of the apparatus 1904 or configured to perform thefunctions recited by the means. As described supra, the apparatus 1904may include the TX processor 368, the RX processor 356, and thecontroller/processor 359. As such, in one configuration, the means maybe the TX processor 368, the RX processor 356, and/or thecontroller/processor 359 configured to perform the functions recited bythe means. In other examples, the means may be the positioning MLcomponent 199 of the network entity 1902 configured to perform thefunctions recited by the means. As described supra, the network entity1902 may include the TX processor 316, the RX processor 370, and thecontroller/processor 375. As such, in one configuration, the means maybe the TX processor 316, the RX processor 370, and/or thecontroller/processor 375 configured to perform the functions recited bythe means.

It is understood that the specific order or hierarchy of blocks in theprocesses/flowcharts disclosed is an illustration of example approaches.Based upon design preferences, it is understood that the specific orderor hierarchy of blocks in the processes/flowcharts may be rearranged.Further, some blocks may be combined or omitted. The accompanying methodclaims present elements of the various blocks in a sample order, and arenot limited to the specific order or hierarchy presented.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not limited to the aspects describedherein, but are to be accorded the full scope consistent with thelanguage claims. Reference to an element in the singular does not mean“one and only one” unless specifically so stated, but rather “one ormore.” Terms such as “if,” “when,” and “while” do not imply an immediatetemporal relationship or reaction. That is, these phrases, e.g., “when,”do not imply an immediate action in response to or during the occurrenceof an action, but simply imply that if a condition is met then an actionwill occur, but without requiring a specific or immediate timeconstraint for the action to occur. The word “exemplary” is used hereinto mean “serving as an example, instance, or illustration.” Any aspectdescribed herein as “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects. Unless specifically statedotherwise, the term “some” refers to one or more. Combinations such as“at least one of A, B, or C,” “one or more of A, B, or C,” “at least oneof A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or anycombination thereof” include any combination of A, B, and/or C, and mayinclude multiples of A, multiples of B, or multiples of C. Specifically,combinations such as “at least one of A, B, or C,” “one or more of A, B,or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and“A, B, C, or any combination thereof” may be A only, B only, C only, Aand B, A and C, B and C, or A and B and C, where any such combinationsmay contain one or more member or members of A, B, or C. Sets should beinterpreted as a set of elements where the elements number one or more.Accordingly, for a set of X, X would include one or more elements. If afirst apparatus receives data from or transmits data to a secondapparatus, the data may be received/transmitted directly between thefirst and second apparatuses, or indirectly between the first and secondapparatuses through a set of apparatuses. All structural and functionalequivalents to the elements of the various aspects described throughoutthis disclosure that are known or later come to be known to those ofordinary skill in the art are expressly incorporated herein by referenceand are encompassed by the claims. Moreover, nothing disclosed herein isdedicated to the public regardless of whether such disclosure isexplicitly recited in the claims. The words “module,” “mechanism,”“element,” “device,” and the like may not be a substitute for the word“means.” As such, no claim element is to be construed as a means plusfunction unless the element is expressly recited using the phrase “meansfor.”

As used herein, the phrase “based on” shall not be construed as areference to a closed set of information, one or more conditions, one ormore factors, or the like. In other words, the phrase “based on A”(where “A” may be information, a condition, a factor, or the like) shallbe construed as “based at least on A” unless specifically reciteddifferently.

The following aspects are illustrative only and may be combined withother aspects or teachings described herein, without limitation.

Aspect 1 is a method of position estimation, including: determining, foreach grid point within a range of an initial position of a UE, a set ofPR residuals based on PRs for each SV of a set of SVs; and determiningan estimated position of the UE based on the sets of determined PRresiduals.

Aspect 2 is the method of aspect 1, where the PRs include a measured PRand a predicted PR, and each PR residual in the set of PR residuals is ameasure of a difference between the measured PR and the predicted PR.

Aspect 3 is the method of any of aspects 1 and 2, further includingmeasuring, for each SV of the set of SVs, the PR between the UE and theSV.

Aspect 4 is the method of any of aspects 1 to 3, further includingpredicting, for each grid point and for each SV of the set of SVs, thePR between the UE and the SV at the grid point.

Aspect 5 is the method of any of aspects 1 to 4, where each PR residualin the set of PR residuals is determined based on a difference between ameasured PR and the predicted PR.

Aspect 6 is the method of any of aspects 1 to 5, further includingdetermining, based on an ML classifier, a likelihood of whether the UEis approximate to the grid point based on the corresponding set ofdetermined PR residuals, where the estimated position of the UE isdetermined based on the determined likelihoods.

Aspect 7 is the method of any of aspects 1 to 6, where the likelihood ofwhether the UE is approximate the grid point is classified by the MLclassifier based on a distribution of PR residuals in each set of PRresiduals.

Aspect 8 is the method of any of aspects 1 to 7, further including:determining for each SV of the set of SVs at least a geometricorientation with respect to the UE; and determining, based on an MLclassifier and the determined geometric orientation with respect to theUE for each SV of at least a subset of the set of SVs, a relative PRweight for each SV of the set of SVs, where the estimated position ofthe UE is further determined based on the relative PR weight for each SVof the set of SVs.

Aspect 9 is the method of any of aspects 1 to 8, where the geometricorientation is a function of at least an azimuth angle and a zenithangle between the UE and a corresponding SV of the set of SVs.

Aspect 10 is the method of any of aspects 1 to 9, where the relative PRweight for one SV of the set of SVs is based on a spherical distancebetween the one SV and each SV of the at least the subset of the set ofSVs, the spherical distance between the one SV and an other SV beingbased on the geometric orientation of the one SV compared to thegeometric orientation of the other SV.

Aspect 11 is the method of any of aspects 1 to 10, where the relative PRweight for one SV of the set of SVs is further based on one or more of acarrier-to-noise ratio, an auto-correlation function, a code-carrierphase consistency, measurement status or error flags, consistencybetween different band measurements of the one SV, weighted leastsquares a-posteriori residuals, or signal integration information.

Aspect 12 is the method of any of aspects 1 to 11, where the relative PRweight is based on a predicted relative PR error by the ML classifier.

Aspect 13 is an apparatus for position estimation for implementing anyof aspects 1 to 12.

Aspect 14 is an apparatus for position estimation including means forimplementing any of aspects 1 to 12.

Aspect 15 is a computer-readable medium storing computer executablecode, where the code when executed by a processor causes the processorto implement any of aspects 1 to 12.

What is claimed is:
 1. An apparatus for position estimation, comprising:a memory; and at least one processor coupled to the memory and, based atleast in part on information stored in the memory, the at least oneprocessor is configured to: determine, for each grid point within arange of an initial position of a user equipment (UE), a set ofpseudorange (PR) residuals based on PRs for each space vehicle (SV) of aset of SVs; and determine an estimated position of the UE based on thesets of determined PR residuals.
 2. The apparatus of claim 1, whereinthe PRs comprise a measured PR and a predicted PR, and each PR residualin the set of PR residuals is a measure of a difference between themeasured PR and the predicted PR.
 3. The apparatus of claim 1, whereinthe at least one processor is configured to measure, for each SV of theset of SVs, the PR between the UE and the SV.
 4. The apparatus of claim1, wherein the at least one processor is configured to predict, for eachgrid point and for each SV of the set of SVs, the PR between the UE andthe SV at the grid point.
 5. The apparatus of claim 4, wherein each PRresidual in the set of PR residuals is determined based on a differencebetween a measured PR and the predicted PR.
 6. The apparatus of claim 1,the at least one processor is configured to determine, based on amachine learning (ML) classifier, a likelihood of whether the UE isapproximate to the grid point based on a corresponding set of determinedPR residuals, wherein the estimated position of the UE is determinedbased on the determined likelihoods.
 7. The apparatus of claim 6,wherein the likelihood of whether the UE is approximate the grid pointis classified by the ML classifier based on a distribution of PRresiduals in each set of PR residuals.
 8. The apparatus of claim 1,wherein the at least one processor is configured to: determining foreach SV of the set of SVs at least a geometric orientation with respectto the UE; and determining, based on a machine learning (ML) classifierand the determined geometric orientation with respect to the UE for eachSV of at least a subset of the set of SVs, a relative PR weight for eachSV of the set of SVs, wherein the estimated position of the UE isfurther determined based on the relative PR weight for each SV of theset of SVs.
 9. The apparatus of claim 8, wherein the geometricorientation is a function of at least an azimuth angle and a zenithangle between the UE and a corresponding SV of the set of SVs.
 10. Theapparatus of claim 8, wherein the relative PR weight for one SV of theset of SVs is based on a spherical distance between the one SV and eachSV of the at least the subset of the set of SVs, the spherical distancebetween the one SV and an other SV being based on the geometricorientation of the one SV compared to the geometric orientation of theother SV.
 11. The apparatus of claim 10, wherein the relative PR weightfor one SV of the set of SVs is further based on one or more of acarrier-to-noise ratio, an auto-correlation function, a code-carrierphase consistency, measurement status or error flags, consistencybetween different band measurements of the one SV, weighted leastsquares a-posteriori residuals, or signal integration information. 12.The apparatus of claim 8, wherein the relative PR weight is based on apredicted relative PR error by the ML classifier.
 13. A method ofposition estimation, comprising: determining, for each grid point withina range of an initial position of a user equipment (UE), a set ofpseudorange (PR) residuals based on PRs for each space vehicle (SV) of aset of SVs; and determining an estimated position of the UE based on thesets of determined PR residuals.
 14. The method of claim 13, wherein thePRs comprise a measured PR and a predicted PR, and each PR residual inthe set of PR residuals is a measure of a difference between themeasured PR and the predicted PR.
 15. The method of claim 13, furthercomprising measuring, for each SV of the set of SVs, the PR between theUE and the SV.
 16. The method of claim 13, further comprisingpredicting, for each grid point and for each SV of the set of SVs, thePR between the UE and the SV at the grid point.
 17. The method of claim16, wherein each PR residual in the set of PR residuals is determinedbased on a difference between a measured PR and the predicted PR. 18.The method of claim 13, further comprising determining, based on amachine learning (ML) classifier, a likelihood of whether the UE isapproximate to the grid point based on a corresponding set of determinedPR residuals, wherein the estimated position of the UE is determinedbased on the determined likelihoods.
 19. The method of claim 18, whereinthe likelihood of whether the UE is approximate the grid point isclassified by the ML classifier based on a distribution of PR residualsin each set of PR residuals.
 20. The method of claim 13, furthercomprising: determining for each SV of the set of SVs at least ageometric orientation with respect to the UE; and determining, based ona machine learning (ML) classifier and the determined geometricorientation with respect to the UE for each SV of at least a subset ofthe set of SVs, a relative PR weight for each SV of the set of SVs,wherein the estimated position of the UE is further determined based onthe relative PR weight for each SV of the set of SVs.
 21. The method ofclaim 20, wherein the geometric orientation is a function of at least anazimuth angle and a zenith angle between the UE and a corresponding SVof the set of SVs.
 22. The method of claim 20, wherein the relative PRweight for one SV of the set of SVs is based on a spherical distancebetween the one SV and each SV of the at least the subset of the set ofSVs, the spherical distance between the one SV and an other SV beingbased on the geometric orientation of the one SV compared to thegeometric orientation of the other SV.
 23. The method of claim 22,wherein the relative PR weight for one SV of the set of SVs is furtherbased on one or more of a carrier-to-noise ratio, an auto-correlationfunction, a code-carrier phase consistency, measurement status or errorflags, consistency between different band measurements of the one SV,weighted least squares a-posteriori residuals, or signal integrationinformation.
 24. The method of claim 20, wherein the relative PR weightis based on a predicted relative PR error by the ML classifier.
 25. Anapparatus for position estimation, comprising: means for determining,for each grid point within a range of an initial position of a userequipment (UE), a set of pseudorange (PR) residuals based on PRs foreach space vehicle (SV) of a set of SVs; and means for determining anestimated position of the UE based on the sets of determined PRresiduals.
 26. The apparatus of claim 25, wherein the PRs comprise ameasured PR and a predicted PR, and each PR residual in the set of PRresiduals is a measure of an excess delay based on a comparison of themeasured PR and the predicted PR.
 27. The apparatus of claim 25, furthercomprising means for measuring, for each SV of the set of SVs, the PRbetween the UE and the SV.
 28. The apparatus of claim 25, furthercomprising means for predicting, for each grid point and for each SV ofthe set of SVs, the PR between the UE and the SV at the grid point. 29.The apparatus of claim 25, further comprising means for determining,based on a machine learning (ML) classifier, a likelihood of whether theUE is approximate to the grid point based on a corresponding set ofdetermined PR residuals, wherein the estimated position of the UE isdetermined based on the determined likelihoods.
 30. A computer-readablemedium storing computer executable code, the code when executed by aprocessor causes the processor to: determine, for each grid point withina range of an initial position of a user equipment (UE), a set ofpseudorange (PR) residuals based on PRs for each space vehicle (SV) of aset of SVs; and determine an estimated position of the UE based on thesets of determined PR residuals.